300 Healthcare Projects based on Python

  • 1 100 beginner-level Python project ideas for Healthcare
  • 2 100 intermediate-level Python project ideas for Healthcare
  • 3 100 expert-level Python project ideas for Healthcare
  • 4 I. Introduction
  • 5 II. Data Handling and Analysis
  • 6 III. Machine Learning for Healthcare
  • 7 IV. Healthcare Data Security
  • 8 V. Healthcare APIs and Integration
  • 9 VI. Electronic Health Records (EHR)
  • 10 VII. Telemedicine and Remote Monitoring
  • 11 VIII. Case Studies and Success Stories
  • 12 IX. FAQs (Frequently Asked Questions)
  • 13 X. Conclusion
  • 14 Python Learning Resources
  • 15 Python projects and tools

100 beginner-level Python project ideas for Healthcare

100 intermediate-level python project ideas for healthcare, 100 expert-level python project ideas for healthcare.

In today’s rapidly evolving healthcare landscape, technology plays a pivotal role in enhancing patient care, optimizing processes, and driving innovation. Python, a versatile and powerful programming language, has emerged as a vital tool in the healthcare sector. In this comprehensive guide, we will explore how Python empowers healthcare solutions by leveraging essential libraries. We’ll also address frequently asked questions to provide you with a holistic understanding of Python’s role in healthcare.

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I. Introduction


The Role of Python in Healthcare

Python’s popularity in healthcare stems from its simplicity, readability, and a vast ecosystem of libraries. It enables healthcare professionals and developers to streamline tasks, from data analysis to building complex machine learning models.

Importance of Choosing the Right Libraries

Selecting the right Python libraries is crucial for the success of healthcare projects. These libraries provide specialized functionalities tailored to healthcare needs, saving time and resources.

Overview of Essential Libraries for Healthcare Projects

Let’s delve into the key Python libraries that are essential for healthcare projects:

II. Data Handling and Analysis

Pandas: managing healthcare data.

Pandas is a cornerstone library for data manipulation and analysis in Python. It allows healthcare practitioners to efficiently manage and analyze patient data, electronic health records (EHRs), and more.

NumPy: Numeric Computations in Healthcare

NumPy excels in performing numerical computations. In healthcare, it aids in tasks such as medical image processing, statistical analysis, and mathematical modeling.

Matplotlib and Seaborn: Data Visualization for Healthcare

Matplotlib and Seaborn provide tools for creating informative visualizations of healthcare data. They help in presenting insights and trends to healthcare professionals and stakeholders.

III. Machine Learning for Healthcare

Scikit-learn: building healthcare models.

Scikit-Learn simplifies machine learning model development. In healthcare, it is used for tasks like disease prediction, patient outcome analysis, and drug discovery.

TensorFlow and Keras: Deep Learning for Healthcare

TensorFlow and Keras are indispensable for deep learning applications in healthcare. They enable the development of neural networks for tasks such as medical image analysis and disease classification.

Natural Language Processing (NLP) in Healthcare

NLP libraries like NLTK and spaCy assist in extracting insights from unstructured healthcare text data. They are instrumental in tasks like sentiment analysis of patient reviews and clinical notes summarization.

IV. Healthcare Data Security

Hipaa compliance: ensuring patient data privacy.

Health Insurance Portability and Accountability Act ( HIPAA ) compliance is paramount in healthcare. Python libraries like PyCryptodome aid in encrypting sensitive patient data, ensuring privacy and security.

Encryption and Cybersecurity in Healthcare Projects

Python’s robust ecosystem includes libraries like cryptography for implementing encryption and enhancing cybersecurity in healthcare applications.

V. Healthcare APIs and Integration

Working with healthcare data apis.

Python’s versatility extends to API integration. Developers can seamlessly work with healthcare data APIs, enabling real-time data retrieval and updates.

Integrating External Data Sources in Python Projects

Python’s adaptability allows for the integration of external healthcare data sources, enriching analyses and enhancing decision-making.

VI. Electronic Health Records (EHR)

Ehr systems in python.

Python-based EHR systems streamline patient data management, making it easier for healthcare providers to access, update, and analyze patient records.

Data Extraction and Analysis from EHR

Python libraries enable the extraction and analysis of valuable insights from electronic health records, contributing to evidence-based medicine and patient care improvements.

VII. Telemedicine and Remote Monitoring

Telehealth applications with python.

Python facilitates the development of telehealth applications, enabling remote consultations, diagnosis, and treatment.

Remote Patient Monitoring Solutions

With Python, healthcare professionals can create solutions for remote patient monitoring, enhancing patient care and reducing hospital readmissions.

VIII. Case Studies and Success Stories

Real-world applications of python in healthcare.

Let’s explore real-world examples of Python-powered healthcare solutions that have transformed patient care and healthcare management.

Examples of Python-Powered Healthcare Solutions

We’ll dive into specific cases where Python played a pivotal role in solving healthcare challenges.

IX. FAQs (Frequently Asked Questions)

What are the key python libraries for healthcare projects.

Python libraries like Pandas, NumPy, Scikit-Learn, and TensorFlow are key for healthcare projects. They enable data handling, analysis, and machine learning.

How can I ensure data security when working with healthcare data in Python?

Ensuring data security involves implementing encryption (e.g., PyCryptodome) and following HIPAA compliance standards.

What are the benefits of using Python for telemedicine solutions?

Python simplifies telemedicine application development, enabling remote healthcare services and improving patient access to care.

Are there any specific libraries for natural language processing in healthcare?

Libraries like NLTK and spaCy are used for natural language processing in healthcare, aiding in text analysis and insights extraction.

How can Python assist in electronic health record (EHR) management?

Can python be used for remote patient monitoring.

Yes, Python can be used to develop remote patient monitoring solutions, allowing for continuous patient data tracking and improved care.

What are the compliance considerations for healthcare projects in Python?

Compliance considerations include adhering to HIPAA regulations, ensuring data privacy, and maintaining cybersecurity.

Are there any notable success stories of Python in healthcare?

Yes, there are several success stories where Python-powered healthcare solutions have improved patient care, diagnosis, and research.

How do I integrate external healthcare data sources into my Python project?

Python’s flexibility allows for the integration of external healthcare data sources through APIs and data connectors.

What are the ethical considerations when working with patient data in Python?

Ethical considerations include obtaining proper consent, anonymizing data, and ensuring the secure handling of patient information.

X. Conclusion

Recap of essential python libraries for healthcare.

In this comprehensive guide, we’ve explored essential Python libraries that empower healthcare solutions, from data analysis to machine learning and data security.

The Promising Future of Python in Healthcare

Python’s future in healthcare looks promising, with continued advancements in technology and a growing community of developers dedicated to improving healthcare outcomes.

Python Learning Resources

  • Python.org’s Official Documentation  â€“  https://docs.python.org/  Python’s official documentation is a highly authoritative source. It provides in-depth information about the language, libraries, and coding practices. This is a go-to resource for both beginners and experienced developers.
  • Coursera’s Python for Everybody Course  â€“  https://www.coursera.org/specializations/python  Coursera hosts this popular course taught by Dr. Charles Severance. It covers Python programming from the ground up and is offered by the University of Michigan. The association with a reputable institution adds to its credibility.
  • Real Python’s Tutorials and Articles  â€“  https://realpython.com/  Real Python is known for its high-quality tutorials and articles that cater to different skill levels. The platform is respected within the Python community for its accuracy and practical insights.
  • Stack Overflow’s Python Tag  â€“  https://stackoverflow.com/questions/tagged/python  Stack Overflow is a well-known platform for programming-related queries. Linking to the Python tag page can provide readers with access to a vast collection of real-world coding problems and solutions.
  • Python Weekly Newsletter  â€“  https://www.pythonweekly.com/  The Python Weekly newsletter delivers curated content about Python programming, including articles, news, tutorials, and libraries. Subscribing to such newsletters is a common practice among developers looking for trustworthy updates.

Python projects and tools

  • Free Python Compiler :  Compile your Python code hassle-free with our online tool.
  • Comprehensive Python Project List :  A one-stop collection of diverse Python projects.
  • Python Practice Ideas :  Get inspired with 600+ programming ideas for honing your skills.
  • Python Projects for Game Development :  Dive into game development and unleash your creativity.
  • Python Projects for IoT :  Explore the exciting world of the Internet of Things through Python.
  • Python for Artificial Intelligence :  Discover how Python powers AI with 300+ projects.
  • Python for Data Science :  Harness Python’s potential for data analysis and visualization.
  • Python for Web Development :  Learn how Python is used to create dynamic web applications.
  • Python Practice Platforms and Communities :  Engage with fellow learners and practice your skills in real-world scenarios.
  • Python Projects for All Levels :  From beginner to advanced, explore projects tailored for every skill level.
  • Python for Commerce Students :  Discover how Python can empower students in the field of commerce.

Intro to deep learning for medical imaging using Google Colab

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Dr. Honey Durgaprasad Tiwari, both the CTO at INKOR Technologies Private Limited, India, and a dedicated academic researcher, brings a wealth of expertise. With a Post-Doctoral stint at Sungkyunkwan University, Ph.D. in Electronic, Information and Communication Engineering from Konkuk University, Seoul, South Korea, and M.Tech in Embedded Electronic Systems from VNIT Nagpur, his research legacy spans wireless power transfer, medical imaging, and FPGA innovation. Notably, he has authored 40+ SCI papers, conference contributions, and patents, leaving an indelible mark on these fields. Holding pivotal Academic Administrative roles, including Head of Department and IQAC Coordinator, he passionately channels his insights into concise and impactful blogs, enriching the tech discourse. 🚀🔬📚

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pyhealth 1.1.4

pip install pyhealth Copy PIP instructions

Released: May 31, 2023

A Deep Learning Python Toolkit for Healthcare Applications

Project links

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View statistics for this project via Libraries.io , or by using our public dataset on Google BigQuery

License: BSD License

Author: Chaoqi Yang, Zhenbang Wu, Patrick Jiang, Zhen Lin, Benjamin Danek, Junyi Gao, Jimeng Sun

Tags heathcare AI, healthcare, electronic health records, EHRs, machine learning, data mining, neural networks, deep learning


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  • Financial and Insurance Industry
  • Information Technology
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  • OSI Approved :: BSD License
  • Python :: 3.8

Project description

PyPI version

PyHealth is designed for both ML researchers and medical practitioners . We can make your healthcare AI applications easier to deploy and more flexible and customizable. [Tutorials]

[News!] Our PyHealth is accepted by KDD 2023 Tutorial Track! We will present a 3-hour tutorial on PyHealth at [KDD 2023] , August 6-10, Long Beach, CA.


1. Installation :rocket:

2. introduction :book:.

pyhealth provides these functionalities (we are still enriching some modules):


You can use the following functions independently:

Building a healthcare AI pipeline can be as short as 10 lines of code in PyHealth .

3. Build ML Pipelines :trophy:

All healthcare tasks in our package follow a five-stage pipeline :


We try hard to make sure each stage is as separate as possible, so that people can customize their own pipeline by only using our data processing steps or the ML models.

Module 1: <pyhealth.datasets>

pyhealth.datasets provides a clean structure for the dataset, independent from the tasks. We support MIMIC-III , MIMIC-IV and eICU , etc. The output (mimic3base) is a multi-level dictionary structure (see illustration below).


Module 2: <pyhealth.tasks>

pyhealth.tasks defines how to process each patient’s data into a set of samples for the tasks. In the package, we provide several task examples, such as drug recommendation and length of stay prediction . It is easy to customize your own tasks following our template .

Module 3: <pyhealth.models>

pyhealth.models provides different ML models with very similar argument configs.

Module 4: <pyhealth.trainer>

pyhealth.trainer can specify training arguments, such as epochs, optimizer, learning rate, etc. The trainer will automatically save the best model and output the path in the end.

Module 5: <pyhealth.metrics>

pyhealth.metrics provides several common evaluation metrics (refer to Doc and see what are available).

4. Medical Code Map :hospital:

pyhealth.codemap provides two core functionalities. This module can be used independently.

5. Medical Code Tokenizer :speech_balloon:

pyhealth.tokenizer is used for transformations between string-based tokens and integer-based indices, based on the overall token space. We provide flexible functions to tokenize 1D, 2D and 3D lists. This module can be used independently.

6. Tutorials :teacher:


We provide the following tutorials to help users get started with our pyhealth.

Tutorial 0: Introduction to pyhealth.data [Video]

Tutorial 1: Introduction to pyhealth.datasets [Video]

Tutorial 2: Introduction to pyhealth.tasks [Video]

Tutorial 3: Introduction to pyhealth.models [Video]

Tutorial 4: Introduction to pyhealth.trainer [Video]

Tutorial 5: Introduction to pyhealth.metrics [Video]

Tutorial 6: Introduction to pyhealth.tokenizer [Video]

Tutorial 7: Introduction to pyhealth.medcode [Video]

The following tutorials will help users build their own task pipelines.

Pipeline 1: Drug Recommendation [Video]

Pipeline 2: Length of Stay Prediction [Video]

Pipeline 3: Readmission Prediction [Video]

Pipeline 4: Mortality Prediction [Video]

Pipeline 5: Sleep Staging [Video]

We provided the advanced tutorials for supporting various needs.

Advanced Tutorial 1: Fit your dataset into our pipeline [Video]

Advanced Tutorial 2: Define your own healthcare task

Advanced Tutorial 3: Adopt customized model into pyhealth [Video]

Advanced Tutorial 4: Load your own processed data into pyhealth and try out our ML models [Video]

7. Datasets :mountain_snow:

We provide the processing files for the following open EHR datasets:

8. Machine/Deep Learning Models and Benchmarks :airplane:

9. citing pyhealth :handshake:, project details, release history release notifications | rss feed.

May 31, 2023

Jan 24, 2023

Dec 14, 2022

Nov 16, 2022

1.0a2 pre-release

Oct 23, 2022

1.0a1 pre-release

1.0a0 pre-release

Jan 11, 2021

Nov 9, 2020

Aug 26, 2020

Aug 13, 2020

Aug 6, 2020

Aug 3, 2020

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></center></p><h2>Python in healthcare: Actual applications with examples</h2><p>Python is a favorite programming language used in lots of majors. One of the fields that enjoy the most benefit from Python is healthcare. With the adoption of Python, healthcare marked a great progress like healthcare apps , hospital operation management, predictive prognosis…and lots of other applications. In this article, we will give you more detailed examples about real applications of python in healthcare. Therefore, you can feel more clear about the influence of Python in the health industry.</p><p><center><img style=

First, we give you some basic information about Python

Python has a application in a wide-ranging field recently. Back to the history, Guido van Rossum first innovated Python and launched it in 1991. Basically, Python is a multifunctional programming language. You can see the adaptation of Python almost everywhere data, mathematical computation, or lines of code. Contrary to Java, Python is not limitation to web development.

Python, like most programming languages, uses an interpreter to carry out the finished lines of code. There are many free resources available to help you learn Python. Then it is one of the easiest coding languages to learn because of basing on English syntax.

Then, why is Python the perfect programming language for Healthcare solutions?

Python and healthcare

Simple and Easy-to-understand syntax

Python is a computer language with a simple and accessible syntax that has appearance widely in healthcare. Even when applying advanced features, developers can write basic code or use plugins to create more plentiful features.

Python is a highly scalable programming language

One of the most important Python advantages in healthcare is scalability. Due to the numerous patients, healthcare institutions may have a big amount of data and a high number of traffic. Python does not lag or crash while working with big amounts of data and information.

Libraries and Community

Python provides a large number of libraries that developers can employ to enhance the mHealth app. Especially, with top 10 best Python libraries for ecommerce , they can totally free from writing code for each characteristic because of a library which can support them. Python projects in healthcare also benefit from a wide community that can help with any problems that emerge. The developers have already answered common Python questions that could hinder the development process.

Because of their stringent health requirements, security is as a top consideration when healthcare firms use digital technology. As a result, Python provides security through its regular updates that address security concerns.

Python’s large library base enables the implementation of such regulations’ needed safety criteria. Python is a great place to start when it comes to healthcare programming languages. Furthermore, it has lots of professionals working and collaborating against any hacking dangers inside its big community.

There are sure to be repetitive duties in every organization which make staffs find them time-consuming. Python makes it simple to harness the power of automation. Python can be a helpful assistance to create applications that relieve healthcare staff of their responsibilities. Thanks to Python programming, repetitive processes can experience the process of automation to enhance efficiencies in an already busy setting.


Simply, anyone can use the tools and software for free. The only catch is that you’d have to develop your own projects. But with the cost-effectiveness of open-source software like Python, this is still a viable alternative for healthcare businesses. This plays an  important role in hospitals, which are typically cash-strapped and resource-conservative. Python is frequently the favorite tool in such situations.

When it comes to healthcare data, security is crucial. When building solutions in this industry, information security is frequently a key consideration. Python, on the other hand, is useful once more because its frameworks let secure data interchange, and its need to integrate data for a perfect experience is validated by HIPAA compliance. As a result, there would be no violations.

Versatility and Flexibility

Python aids in the development of both online and offline apps for a number of purposes. Even though this is nothing new because other programs accomplish the same thing, it is highly favourite in the healthcare sector due to its versatility. In this industry, intuitive applications that cater to a wide range of needs are significantly more important than in any other. Emergencies can happen at any moment and in any situation, and if the coding of an application is in a sophisticated way, it can lead to some undesirable outcomes.

Now we list some applications of Python in healthcare which can make you surprise

some applications of Python in healthcare

Healthcare Apps

The most important element that healthcare organizations must support everyone is 24-hour availability. This functionality is simple to implement using online or offline mobile and web applications.

The Python programming language can assist developers in creating simplified and user-friendly healthcare applications. For example, Medication tracking and reminder app development is an successful application of Python which allow users to contact specialists or track their health and fitness at any time and from any location.

Medical Image Diagnostics

Doctors’ decisions in medical diagnostics are mostly reliant on the results of medical imaging. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans are two examples. Traditionally, doctors would perform these tasks manually, using their own eyes to scan the numerous photographs.

This is where Python’s machine learning applications are really outstanding. In some case, Python can be as a data-handling programming language. Then an AI model will receive sets of instructions that employ machine learning to scan these photos automatically. These photos are analyzed, and doctors are given reports with possible diagnoses. All of these computer-assisted diagnoses are supported by AI and Python.

The AI model was able to correctly predict breast cancer diagnosis with 92 percent accuracy in a study conducted by Harvard Medical School. The computer will take over the responsibility of discriminating between cancers and healthy areas. Doctors can use this high level of accuracy as a reference point for making diagnoses.

Hospital Operations Management

Because patients’ lives are in the hands of healthcare personnel, efficiency is critical at hospitals. But, until recently, most healthcare organizations, such as hospitals, lacked such digital infrastructure. The management of doctors, nurses, and other auxiliary staff, as well as their appropriate patient assignments, takes place around the clock in hospitals.

This management process can be rather chaotic, but over the last several years, things have been going in the correct way thanks to the employment of Python. Through the efforts of data scientists, Python is beginning to be used in hospitals for business intelligence. Data science with Python allows hospital operations have a visualization in an easy-to-understand manner, allowing management teams to better optimize healthcare staff allocations.

For More Effective Patient Care

Managing patients takes a lot of time. In healthcare facilities with limited staff, patients, appointments, and treatments cannot be handled all at once. Python healthcare application development enables the institution to take a more technological approach to patient management, allowing employees to focus on other critical activities.

Patients can use Python to make appointments, get answers to basic queries, place drug orders, contact clinicians in an emergency, and update their health information. Because of this holistic approach to patient management, staff will be able to devote more time to treating patients with serious conditions. Because Python is scalable, dynamic, and user-friendly, stakeholders will find it easier to use a Python healthcare application. Any healthcare application will necessitate a secure programming language that can display its skills while also safeguarding patient data. It gives healthcare facilities extra help, helping the system to work more smoothly as a whole.

Medicine Discoveries

Python’s application isn’t limited to direct interactions with patients. Even from the laboratories, the influence on healthcare can be felt. Given the requirement to create medicine that is both safe and effective, the process of generating medicine can be lengthy. Generally, these drugs have been developed by a manual matching procedure in which potential compounds for a given ailment are evaluated against.

Bioinformaticians and healthcare research scientists are beginning to apply computational ways to replace such manual effort, thanks to the use of Python.This could potentially cut down on the time it takes to find a good match that leads to actual drugs.

Predictive Prognosis

Disease prediction analytics is the most crucial benefit of Python programming in healthcare. Python may be used to build Machine Learning models which can forecast diseases before becoming serious.

It’s tough to predict the fate of any sickness. The majority of today’s systems are ineffective in envisioning what will occur next. Based on EHR data, doctors can utilize Python-based healthcare data analytics to estimate the optimum treatment plan or death. Google’s Deep Learning and Machine Learning system, for instance, may foresee cancer in people using medical data and history. The use of Python programs for disease prediction speeds up the therapeutic process, allowing physicians to avoid any major complications.

Faster Protection from Diseases

Medicines are the only items that can assist you cure or avoid a disease. However, the process of producing a drug is extensive. Traditional approaches include a trial-and-error process of combining various compounds in order to find the most efficient and safe treatment for any illness. Python and other programming languages can turn this time-consuming procedure into a digital one. Python, for example, may do several iterations on various substances using a computerized approach, allowing scientists to quickly produce an effective treatment.

And how Medical Startups are using Python?


With a New York-based business backed by venture capitalists and the NIH, it is utilizing artificial intelligence to reduce the distance between diseases and successful therapies.

This company monitors patients’ responses to treatment using computer vision and pill recognition technology. They employ AI to “see, hear, and understand how patients react to cure,” as they put it. Built using Django and bespoke Python-based scripting, the interactive medical assistant collects data from audio and visual interactions with patients to measure adherence to therapy and forecast treatment failings.

Face recognition and pill identification technology, for example, aid in ensuring that patients are taking the correct medication.



Takes over biopharmaceutical and medical device firms which need to make judgments, offer therapies, and get the best potential patient outcomes using machine learning (artificial intelligence) and complete contextual data. Roam’s platform is driven by a proprietary data asset called Health Knowledge Graph, which is constantly supplemented with new data utilizing natural language processing.


It is a web and cloud-based patient care stage that offers software as a service. This American corporation provides management and medical billing services to doctors and patients and makes electronic health information available digitally. This is mostly a phone and web-based platform.



Basically, Fathom Health is a San Francisco-based medical firm that reads, analyzes, and structures datasets in electronic health records using deep learning and natural language processing (NLP) models.

They employs AI models to quickly and accurately code millions of information from patient charts and billing records. Fathom Health uses Python open-source frameworks to secure PHI in real time, which is combined with their de-identification technology.


They are a California-based firm, offers a real-time prediction software that assists hospitals in forecasting patient influx, initiating relevant therapies, and allocating hospital resources appropriately. Besides, Qventus’ backend apps are written in Python and Java. It is a real-time forecasting technology that assists hospitals in planning for patient influx. Healthcare providers have reported an average reduction of 11% in OR case delays, a 15% decline in length of hospital stays, a 40% cutback in needless lab investigations, and enhanced patient satisfaction using Qventus’ ML-based forecasting approaches.

Lastly, we give you a picture about the future of using Python in Healthcare

Precision medicine is likely to be an impactful examples of employing AI/ML which is one of top 8 big healthcare technology trends 2022 . Accuracy medicine aims to provide patients with exact treatment options based on their medical history, genetic information, lifestyle decisions, and constantly changing pathology testing. The overall goal is to produce precision medical tools by combining the most powerful AI approaches (such as deep neural networks, search algorithms, reinforcement learning, probabilistic models, supervised learning, and others).

Python is at the forefront of the quest for accuracy medicine, with developers creating AI tools to model why and in what circumstances diseases are more likely to occur using early screening or routine exam data. This is critical for educating and directing healthcare providers to intervene even before symptoms appear. Other very intriguing prospects for using Python to develop AI/ML applications include digital surgery robots. Think about a patient entering an operating room where robots perform exact treatments on them securely and correctly.

Smart robots powered by AI could work alongside doctors, utilizing distributed data-driven insights and guidance based on operation histories and outcomes. Python’s applications in healthcare are limitless, with the possibility of telemedicine and remote surgery for minor operations in the future.

Through this analytics, we give you an outline about the instance of Python used in healthcare. With the assistance of Python, the health industry gains more achievements. This will affect not only doctors, hospitals but also the citizens. People can benefit from this invention with easy access to health service although they can be faraway from the center or don’t have a standard to go to the hospital. Besides, doctor can reduce a little bit about the burden of work when have to handle with the ton of patient along with paperwork. And in the future, Python will continue to be a trend which can adapt in different fields.

In case you want to develop a app by Python, come to us. Experienced experts at  AHT Tech  will bring the best  Mobile Application Development Services  to make you satisfied. . If you want to know more about this, don’t hesitate to contact us .

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Python for Healthcare

Class sessions, class description.

Most Technology Training classes will be delivered online until further notice. Before each sesson, Tech Training will provide a Zoom link for live online classes, along with any required class materials.

This six-hour course provides a better understanding of how Python can be leveraged to assist Healthcare professionals in the practice of medicine, encompassing Clinical Research, Patient Care, and Hospital Management. Python is an open-source (free) programming language that is used in web programming, data science, artificial intelligence, and many scientific applications. Learning Python allows the programmer to focus on solving problems, rather than focusing on syntax.

Pre-Requisites: Users will be expected to be comfortable with downloading and running programs on their computers. Some experience with Terminal is desired. No prior experience with Python is necessary for the course.

Target audience: Physicians, Medical Researchers, and Hospital administrators

Course goal: For participants to develop a better understanding of how Python can be leveraged to assist Healthcare professionals in the practice of medicine. The course will be divided into three topic areas encompassing Clinical Research, Patient Care, and Hospital Management.

Scope: By the end of this course participants will have had a chance to practice the basics of working with Python with hands-on experience developing some real-world applications, all while equipping themselves with the context and awareness for how such tools are leveraged in state of the art solutions.

Course Details:

This six-hour course will be broken into three modules.

Module 1: Leveraging Python's Data Analytics for Clinical Research

Python was born as a language for data analytics, and its core functionality still revolves around manipulating data. For researchers, Python can be an invaluable tool in their arsenal. In this module we will showcase its potential to work on clinical data. We will introduce you to some of the powerful applications Python has for handling and manipulating data, conducting statistical analyses, and generating graphs and other visuals. We will be leveraging sample clinical data to power real examples throughout the session.

Data Analytics

  • NumPy arrays
  • Pandas DataFrames
  • Importing data from CSV files
  • Regression analysis
  • T-Test, P-Test, R2 values etc.
  • Data Analysis of a Clinical Trial dataset

Module 2: Predictive Modeling and Patient Care

In this module we will explore how python's extensive tools can harness the power of Machine Learning for the practice of patient care. We will provide you with a brief overview of some of the most powerful machine learning approaches and then employ python libraries to conduct end-to-end machine learning exercises. This module will culminate in us creating a model to predict disease progression in patients, and diagnose a disease based on symptoms.

Introduction to Machine learning and predictive analyses

  • Key machine learning concepts for classification and regression
  • Data preparation
  • Linear Regression
  • Logistic regression
  • Support vector machines, Random Forests
  • Neural Nets
  • Measuring success of the model
  • Feature selection
  • Miscellaneous

Case Studies:

  • Predictive Model of Disease Progression
  • Classification Model of Disease Diagnosis

Module 3: Simulations Management and Hospital Administration

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8 Healthcare Machine Learning Project Ideas for Practice in 2024

Practice these top healthcare machine learning projects to learn how ML can transform both patient care and the administrative processes in healthcare.

8 Healthcare Machine Learning Project Ideas for Practice in 2024

Will machine learning replace the jobs of doctors or instead give us better health in the coming years? Some researches and studies have shown that machines surpass humans in the diagnosis of diseases. No doubt, machine learning algorithms do better at disease diagnosis, but it is still far from replacing doctors. Let us look at how machine learning is revolutionizing the healthcare sector.

A tool developed by the Houston Method Research Institute developed to detect breast cancer has shown the ability to see mammograms with up to 99% accuracy and give diagnostic information thirty times faster than the average human. Such tools can also reduce the need for biopsies.


Medical Image Segmentation Deep Learning Project

Downloadable solution code | Explanatory videos | Tech Support

Researchers at Purdue University in Indianapolis have developed a machine-learning algorithm that predicts the relapse rate for myelogenous leukemia with 90% accuracy. Researchers in the University College Hospital, London, are developing algorithms using Google’s DeepMind Heath to differentiate between healthy cells and cancerous cells to ensure more targeted radiation treatment for cancerous cells.

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A team comprising researchers from the United States and Ireland have built data visualization tools to display the side effects of drugs using articles from medical journals and analyzing comments on social media. They conducted a study on Adverse Drug Events using predictive analysis, text mining , and neural networks to find comments specific to a drug and its side effects.

Table of Contents

  • Patient Risk Identification
  • Pattern Imaging Analytics
  • Clinical Trial Research
  • Predicting Epidemics
  • Maintaining Healthcare Records
  • Personalized Treatment
  • Robotic Surgery
  • Improved Radiotherapy

FAQ’s Related to Machine Learning in Healthcare 

8 machine learning projects in healthcare sector.

It has been found that the average nurse in the United States spends almost 25% of their time working on regulatory and administrative activities, which can be easily fastened using technology. A survey conducted by the Harvard Business Review found that over 300 healthcare executives felt problems with patient engagement. Cancer is the leading cause of death worldwide and was predicted to account for almost 10 million deaths in 2020 . Time has shown that we can reduce the spread and fatality of cancer with early detection and treatment. You can treat diseases such as Parkinson’s and Alzheimer’s, too, with early detection. 

Explore some innovative machine learning projects in healthcare to understand how machine learning can help combat global health issues and concerns.

Machine Learning Projects in Healthcare

1. Patient Risk Identification

With traditional patient risk identification techniques, it was found that the number of different combinations of variables about conditions, lab values, socioeconomic information, and other data points corresponding to individuals made it very challenging to identify the relationship among the data points. Machine learning models can address some of the weaknesses of traditional linear models as they are better at handling nonlinearity and can better identify implicit relationships among the input variables. Machine learning models can handle feature selection, which is a process that is used to determine the variables and relationships to be considered while building the model. Hence, machine learning algorithms can be used to identify high-risk patients based on historical data, assuming a high number of variables and data points.

Evolution of Machine Learning Applications in Finance : From Theory to Practice

New Projects

Machine Learning Project Idea for Practice: Heart Disease Prediction Project Using Machine Learning

A stroke occurs due to some brain cells’ sudden death due to a lack of oxygen supply to the brain. Oxygen supply is affected when the blood cannot flow to the brain either because of a blockage or rupturing of an artery connected to the brain. The World Health Organization (WHO) ranks stroke as the second leading cause of death globally. Early prognosis of signs of stroke and stroke diseases can help high-risk patients make lifestyle changes. This project aims to analyze the factors that can be classified as “high-risk” for stroke and the relationship between them. You can use the method of logistic regression to identify various factors. 

2. Pattern Imaging Analytics

Machine learning techniques find applications in radiology to help doctors identify any subtle changes in scans that can be missed by the human eye to diagnose health issues at early stages. Scientists have developed deep learning models by training them on previously captured radiographic images to identify the early development of tumors in the brain, lungs, breasts, and other organs. The algorithms are trained to recognize complex patterns in radiographic images. Machine learning tools help radiologists improve their patient care, making them better at their jobs while not replacing them. The use of pattern recognition and segmentation techniques on images such as retinal scans, bones, internal organs, and pathology slides enable faster diagnoses and better tracking of disease progression.

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Machine Learning Project Idea: Image Segmentation Project for Brain Tumor Prognosis

This project aims to accurately diagnose brain tumors to ensure that the patient is treated accordingly. You can use convoluted Neural Networks to perform semantic segmentation. Convoluted Neural Networks or CNNs are a type of deep artificial neural network used extensively in computer vision for image segmentation. Most CNNs use two-dimensional kernels, but some can use three-dimensional kernels and hence fully access the three-dimensional structure of medical images. Medical image segmentation poses additional challenges, such as the scarcity of labeled data and the high memory demand of three-dimensional medical images. 

Source Code - Medical Image Segmentation Deep Learning Project

3. Clinical Trial Research

Clinical development is on an upward trajectory of improvement with the introduction of new digital sources and larger computing power which allows identification of more meaningful patterns in the data which are more clinically relevant. Artificial intelligence and machine learning algorithms help to identify better recommendations for incorporating computational evidence into clinical trials and health care. Clinical trials in the biotechnology industry, regulators, and nonprofit healthcare foundations benefit from the application of machine learning . Technologies such as next-generation sequencing have helped to understand disease mechanisms in a larger pool of patients and can help better approach academic research. Combining genomic and clinical data with the help of machine learning can help develop newer, more useful predictive models .

Machine Project Idea: Predicting Clinical Trial Terminations

In this project, the idea is to use machine learning to understand and determine what factors lead to the termination of a clinical trial. The aim is to identify factors that lead to clinical trials getting terminated and to accurately predict whether a given clinical trial will get terminated or not. A clinical trial can employ better techniques to ensure that it is not terminated by determining the relevant factors. A solution approach is to use feature engineering to identify the relevant factors. You can use sampling and ensemble learning to predict the probability of clinical trial termination.

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4. Predicting Epidemics

The availability of huge computational power makes it possible to use Big Data to predict and manage epidemic outbreaks. The goal is to determine and analyze the spread of an epidemic in specific areas. This can be particularly useful to villages and other areas where healthcare facilities are not readily available. Machine learning models can be built that predict the nature of the spread of an epidemic in an area and also determine where the next outbreak of an epidemic is most likely to occur. Factors such as geography, climate, demographics, and distribution of an affected area’s population have to be taken into account while training the machine learning model so that you can identify other areas prone to outbreaks.

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Machine Learning Project Idea: Predicting the Growth and Trend of COVID-19

This project aims to determine the pattern of growth that is likely to be followed in COVID-19 cases in a particular area. The model must be trained based on the available data in countries and localities that have already been affected and determining factors that increase the number of COVID 19 cases. The robust curve fitting can be used, ensuring that low weightage is given to outliers. The algorithm has to be built to adapt and offer more weightage to recent data since COVID-19 is still not a pandemic of the past but is still prone to changes in how it affects a group of people.

5. Maintaining Healthcare Records

Machine learning techniques can simplify the maintenance of healthcare records, especially electronic health records (EHRs). Using artificial intelligence methods in EHR management can help improve patient care and optimize operations. For example, natural language processing can capture and record a physician’s note and eliminate the manual process. In EHR management systems, machine learning can make it easier to provide clinical decision support, automated image analysis, and integrate telehealth technologies. To effectively apply machine learning techniques to healthcare records, healthcare informatics professionals must maintain data integrity by classifying and cleansing the data.

Machine Learning Project Ideat: De-Identification of Medical Records using Machine Learning

De-identification of medical records identifies protected health information (PHI) and keeps it secure. It is crucial when electronic health records (EHRs) have to be shared for any research purposes. The project aims to eliminate the manual process of de-identification of medical records and to automate the process. One major challenge is finding a diverse dataset to train the model since the PHI data is confidential. The model has to ensure the privacy of the PHI while also creating a shareable representation of the medical text. This ensures that the shareable information can be used when required for any research. A robust de-identification classifier has to be built to separate the private information from the data that you can publicly access.

6. Personalized Treatment

All bodies are not clinically similar, and hence individuals react to medical treatments differently. Personalized medicine requires a customized approach to ensure the health of a particular individual, which will vary from another individual with similar health conditions. Usage of machine learning algorithms opens up the possibility of finding medicines specifically tailored for an individual based on clinical, laboratory, genetics, nutrition, geography, and lifestyle-related data. Multi-modal data can perform a deeper analysis of large datasets and significantly improve human health understanding.

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Machine Learning Project Idea: Personalized Doctor Recommendation System

This project aims to make recommendations for patients seeking medical care beyond physical treatment. The doctor-patient connection is vital for the patient's well-being and can significantly impact both parties. The goal here is to build a system to recommend doctors to patients by identifying similarities among patients who can build a good rapport with a doctor. Patients will have a profile that includes general information such as name, sex, age, ethnicity, preferred language of communication, health conditions, etc. Doctors will have profiles containing details such as their area of specialization, special skills, availability, and any establishments with which the doctors are associated. The system would ideally take feedback from both the doctor and the patient to build the recommendation system.

7. Robotic Surgery

Though robotics in surgery has been around for a while, it is only recently getting integrated with machine learning and artificial intelligence. Machine learning is currently being integrated into robotic surgery for some purposes, such as the automation of suturing and improving surgical materials. Machine learning is also being used as an evaluation tool to monitor the surgical skills of doctors. Some surgeries, especially neurosurgeries, require a sensitive approach and also go on for long durations. Even the best of surgeons can end up fumbling. Since it is a matter of life and death, training robots to perform the surgeries is likely to guarantee more precision.

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Machine Learning Project Idea: Robotic Instrument Segmentation

Robotic-assisted surgeries can be carried out once the semantic segmentation of robotic instruments is properly handled. One main challenge here is that it gets difficult to accurately determine the instrument’s position for tracking during video feeds of surgeries. The instrument has to be accurately segmented pixel-wise. This requires the application of deep neural network algorithms to address the binary segmentation problem. Every pixel in an image has to be labeled either as the instrument or the background from the surgery video. Furthermore, the multi-class segmentation too has to be addressed to differentiate between different parts of the same and different instruments.

8. Improved Radiotherapy

Machine learning has shown immense improvements in the field of radiology. Medical image analysis involves multiple discrete variables that can be analyzed more efficiently with machine learning. Machine learning-based algorithms can learn from many disparate data samples and can hence better identify the relevant variables. In medical image analysis, machine learning can classify lesions into different categories, such as normal or abnormal or benign or malignant. Algorithms are being developed to differentiate between healthy cells and cancer cells and better determine the prognosis of cancer cells so that more efficient radiation treatment can be performed.

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Machine Learning Project Idea: Predicting Individual Radiosensitivity Based On Telomere Length

This project aims to predict how a cancer patient will respond to radiotherapy and the patient’s risk of developing any adverse side effects to the radiotherapy. In this project, you will analyze how the telomere length and the arrangement of chromosomes of individuals affect their response to radiotherapy. Telomeres are a compelling biomarker concerning individual radiosensitivity and risk. As a result, doctors can modify the kind of treatment to be offered to a cancer patient accordingly if it is found that radiotherapy can pose more side effects or is not as beneficial as expected. The training model has to be built taking the telomere length of cancer patients and their response to radiotherapy.

Machine learning has drastically improved many sectors. It appears that we will be witnessing a revolution in the healthcare industry as well. From the use cases and applications of machine learning as mentioned above, it is more likely that more jobs will be created rather than lost due to artificial intelligence and machine learning in the healthcare industry. Health care professionals will find that their jobs are made easier with the help of technology rather than having to worry about losing their jobs.

Explore some out-of-the-box solved end-to-end machine learning projects along with the source code and datasets to learn how machine learning is leveraged across Retail , Healthcare, Finance , and other industries.

1. How can machine learning be used in healthcare?

Machine learning can revolutionize healthcare. It has several applications that can make the work of physicians, nurses, and other healthcare workers easier and save the lives of patients. You can use machine learning for the early detection of diseases and better analysis of their prognosis. You can also use it to identify risks in patients and provide them with customized treatment. 

2. Why is machine learning used in healthcare?

Machine learning tools are built to handle large datasets and find patterns in data that traditional techniques cannot. Historical data makes it possible to leverage machine learning to identify disease patterns and perform medical image analysis to better diagnose and treat patients. It can be used in the early detection and treatment of various health problems.

3. Do hospitals use machine learning?

Machine learning makes it possible to smoothen the administrative processes in hospitals, making it easier to handle EHR’s (electronic healthcare records) and making it easier for patients to receive the treatment and attention they require. You can also use it to identify risk factors for a patient to ensure that patients can receive customized treatment plans based on their requirements. 

4. What are the best machine learning projects in healthcare?

Here is a list of 8 machine learning projects that you can work on related to healthcare:

Heart Stroke Prediction Project Using Machine Learning

Brain Tumor Segmentation

Predicting Clinical Trial Terminations

Predicting the Growth and Trend of COVID-19

De-Identification of Medical Records using Machine Learning

Personalized Doctor Recommendation System

Robotic Instrument Segmentation

Predicting Individual Radiosensitivity Based On Telomere Length

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Welcome to the NHS Python Community for Healthcare

Find out how you can get involved.

Lead by enthusiasts and advocates, the NHS Python Community for Healthcare is an open community of practice that champions the use of the python programming language and open code in the NHS and healthcare sector

Why Python?

Python is an open source, state of the art programming language, used by developers, data scientists, and data engineers. Python's widespread adoption is, in part, due to its accessible 'human readable' syntax, making it relatively easy to pick up and learn.

Along with other open source analytics tools such as R, Python will be essential to drive the 21st century digital transformation of health care.

Principles and aims of the Python Community

  • Promote the use of python in the healthcare by celebrating success and recognising contributions across the community
  • Reduce barriers to entry by highlighting best practice with regard to software, IT operations, security, and coding in the open
  • Open source and publish code to public GitHub repositories under appropriate licences (such as MIT, OGLv3, and GPLv3) alongside suitable open datasets or synthetic data so that our work can be further developed, re-used, and improved upon by everyone in the community
  • Break down silos of expertise and improve technical communication and collaboration across the NHS, health and social care sectors
  • Champion diversity, inclusion, and representation in tech by making coding accessible to all members of the community regardless of background or current level of ability

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Top Machine Learning Projects for Healthcare

In this transformative realm of healthcare, Are you ready to journey through the incredible world of machine learning and its groundbreaking impact on healthcare? In this era where technology and medicine intertwine more closely than ever, machine learning stands out as a beacon of innovation, pushing the boundaries of what’s possible in patient care, diagnosis, and treatment.

This article can be your golden ticket to understanding the Top Machine Learning projects for Healthcare making waves in the healthcare sector in 2024.


Machine Learning Projects for Healthcare

Table of Content

Introduction to Machine Learning in Healthcare

Top 10 machine learning projects for healthcare, 1. medical diagnostics, 2. parkinson’s disease detection, 3. breast cancer diagnosis, 4. cancer cell classification, 5. heart disease prediction, 6. lung cancer detection, 7. pneumonia detection, 8. skin cancer detection, 9. detecting covid-19, 10. health records improvement.

When we talk about humans, their health comes along with them. The global population is aging, and day-to-day lifestyle changes, such as unhealthy diets and lack of physical activity, have contributed to the prevalence of diseases like obesity and diabetes and many chronic diseases and the need for long-term care. The demand for high-quality healthcare services is on the rise, fueled by increasing incomes and growing health awareness. In this context, technologies providing efficient, helpful, and rapid health analysis are highly sought after. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as dominant forces across various industries, continually making headlines with their innovative applications. While Machine Learning for healthcare has gained prominence in finance and banking, its influence extends to diverse sectors, including healthcare. With substantial data generated for each patient, machine learning algorithms hold immense potential in the healthcare landscape. Recognizing the transformative capabilities of Machine Learning projects for healthcare.

Healthcare providers are harnessing machine learning to enhance patient outcomes, streamline disease diagnosis, analyze Electronic Health Records (EHRs) , reduce medical costs, and optimize operational efficiency. According to a Grand View Research report, the global machine learning market in healthcare is projected to reach $31.65 billion by 2025, showcasing a notable CAGR of 44.5% from 2019 to 2025 . The increasing adoption of machine learning applications in healthcare is reshaping both patient care and administrative processes within the industry. Here is a list of the Top 10 Machine Learning Healthcare Projects that underscore ML’s potential to outperform human capabilities, particularly in disease diagnosis, where algorithms exhibit superior proficiency in detecting diseases.

Medical diagnostics have become increasingly important in modern healthcare, as they provide invaluable insights that can help medical doctors detect and diagnose diseases and Machine learning (ML) is playing a transformative role in advancing it. Through the utilization of ML algorithms , medical professionals can leverage vast datasets, including medical images, patient records, and clinical notes, to enhance diagnostic accuracy and efficiency. ML excels in image recognition tasks, making it particularly valuable in medical imaging diagnostics. Algorithms can analyze complex medical images such as X-rays, MRIs, and CT scans , aiding in the identification of anomalies and assisting healthcare practitioners in making more precise diagnoses. Image recognition algorithms have shown great success in recognizing patterns to spot diseases, for example, to help physicians identify minor changes in tumors to detect malignancy. In the realm of medical diagnostics, ML holds the potential to streamline workflows, reduce diagnostic errors, and optimize patient outcomes. By embracing these technological advancements, healthcare providers can offer more accurate and timely diagnoses, ultimately improving the overall quality of healthcare.

Here is a project for your reference: Disease Prediction Using Machine Learning

Parkinson’s disease (PD) is a prevalent neurological disorder impacting muscle movement, affecting mobility, speech, and posture with symptoms such as tremors, muscle rigidity, and bradykinesia. This condition results from neuronal death, causing a decline in dopamine levels in the brain. The reduction in dopamine adversely affects synaptic communication, leading to impaired motor functions. Early detection of PD is crucial for effective treatment, enabling patients to maintain a normal life. With the global increase in the aging population, there is a growing emphasis on the importance of early, remote, and accurate PD detection. Machine learning techniques, including voice analysis, handwriting analysis, and movement sensors, present non-invasive and potentially cost-effective alternatives to traditional diagnostic methods like PET scan s. Personalized treatment plans can be developed through machine learning models tailored to individual patient data, enhancing the potential for more targeted and effective interventions.

Here is a project for your reference: Parkinson Disease Prediction

Machine learning revolutionizes breast cancer diagnosis with advanced tools for early detection and precise predictions. These models analyze diverse data sources, including mammography, ultrasound, and MRI, to identify patterns and anomalies, enhancing the accuracy of diagnoses. Computer-aided diagnosis (CAD) systems, powered by machine learning, assist radiologists by highlighting potential areas of concern in medical imaging, optimizing diagnostic capabilities . Additionally, machine learning utilizes large datasets encompassing patient demographics, medical history, and genetic factors to create predictive models for assessing breast cancer risk. This proactive approach enables personalized screening strategies and interventions for individuals at high risk. In essence, machine learning applications in breast cancer diagnosis empower healthcare professionals with innovative solutions for improved accuracy, rapid detection, and more effective treatment strategies, ultimately leading to better patient outcomes.

Here is the project for your reference: Breast Cancer Wisconsin Diagnosis Breast Cancer Wisconsin Diagnosis using KNN

Cancer cell classification using machine learning is a groundbreaking approach in oncology that involves the application of advanced algorithms to categorize cancer cells into distinct subtypes. This process is pivotal for gaining a comprehensive understanding of tumor diversity, aiding in the development of tailored treatment strategies. Machine learning models, particularly convolutional neural networks (CNNs), are instrumental in image-based classification. In this project uses approach that leverages the Breast Cancer Wisconsin (Diagnostic) dataset, which includes data on tumor attributes such as radius, texture, and perimeter. After installing necessary Python modules , loading and organizing the dataset, and splitting it into training and test sets. It then details the process of building a machine learning model using the Naive Bayes algorithm, a choice driven by its effectiveness in binary classification tasks. The model’s accuracy, evaluated at approximately 94.15%, demonstrates the potential of machine learning in enhancing diagnostic processes. This practical example not only highlights the steps involved in applying machine learning for medical diagnostics but also underscores the broader implications of technology in transforming healthcare outcomes through data-driven insights.

Here is a project for your reference: Cancer cell classification using Scikit-learn

Leveraging machine learning for heart disease detection represents a groundbreaking approach in modern healthcare. Advanced algorithms, such as decision trees, support vector machines , and neural networks, are employed to scrutinize extensive datasets encompassing patient demographics, medical histories, and diagnostic test results . This data-driven methodology enables the identification of intricate patterns and correlations, facilitating early diagnosis and personalized treatment plans. This project outlines a methodical approach to predict heart disease using ANN, a type of deep learning model that mimics neural networks of the brain. This process begins with importing essential libraries in Python, such as TensorFlow and Keras, and progresses through data preprocessing, model building, and evaluation stages. The dataset used includes 13 attributes like age, sex, and cholesterol levels, serving as independent variables to predict the presence of heart disease. The ANN model is meticulously constructed with an input layer, hidden layers with activation functions, and an output layer, optimized using the ‘adam’ optimizer and ‘binary_crossentropy’ loss function. The model’s performance, achieving an accuracy of approximately 85% , underscores the potential of ANN in enhancing diagnostic accuracy and facilitating early intervention strategies. This project highlights accuracy and facilitating the border implications of machine learning in transforming healthcare.

Here is a project for your reference: Heart Disease Prediction using ANN

Machine learning’s role in lung cancer detection is reshaping medical diagnostics. Advanced algorithms, including support vector machines and convolutional neural networks, analyze diverse datasets like medical images and genetic information. These models excel in early and accurate identification of lung cancer, particularly in analyzing chest X-rays and CT scans f or subtle abnormalities. Early detection through machine learning significantly improves treatment outcomes. Machine learning into lung cancer detection not only enhances diagnostic accuracy but also enables personalized treatment plans. Tailoring interventions based on individual genetic makeup and risk factors maximizes efficacy and minimizes side effects. In summary, machine learning applications in lung cancer detection promise to revolutionize pulmonary diagnostics, offering a proactive and personalized approach to combat this prevalent health condition.

Here is the project for your reference: Lung Cancer Detection

The landscape of pneumonia detection has witnessed a revolutionary transformation through the integration of cutting-edge technologies such as deep learning and machine learning. These sophisticated methodologies, notably leveraging convolutional neural networks (CNNs) and support vector machines (SVMs), have showcased exceptional precision in scrutinizing medical imagery like chest X-rays and CT scans . Machine learning models undergo rigorous training on expansive datasets, acquiring an adept understanding of nuanced patterns and variations associated with pneumonia. This enables these algorithms to swiftly and accurately identify abnormal conditions within the lungs, providing clinicians with invaluable support for prompt diagnosis and intervention. The incorporation of deep learning, particularly through CNNs, introduces a layer of automation in feature extraction from medical images.By taking into account patient histories, demographic information, and other relevant factors, these models assist in forecasting potential pneumonia risks. This proactive approach empowers healthcare providers to implement preventive measures and tailor treatment plans based on the unique profiles of individual patients. This amalgamation promises not only enhanced accuracy and efficiency in identifying pneumonia but also a more personalized and patient-centric approach to managing and mitigating this respiratory condition, ultimately resulting in improved overall patient outcomes.

Here is a project for your reference: Pneumonia Detection

The landscape of skin cancer detection has undergone a transformative shift, driven by cutting-edge technologies like machine learning and deep learning. These advanced methodologies, prominently featuring convolutional neural networks (CNNs) and support vector machines (SVMs) , showcase remarkable prowess in scrutinizing dermatological imagery for the early identification of skin cancer. Machine learning models, honed on diverse datasets encompassing a broad spectrum of skin conditions, exhibit a keen ability to discern subtle patterns indicative of malignant lesions. Analyzing features such as asymmetry, border irregularities, color variations, and diameter within images, these algorithms deliver rapid and accurate assessments, proving invaluable to dermatologists for early diagnosis. By considering patient history, demographic factors, and other pertinent information, these models assist in forecasting potential risks, facilitating preventive measures and personalized treatment plans tailored to individual patient profiles. The powerful combination of machine learning and deep learning ensures not only enhanced accuracy in identifying skin cancer but also enables a proactive and personalized approach to managing and mitigating this serious health concern. The result is improved patient outcomes, with the technology proving to be a game-changer in the ongoing battle against skin cancer.

Here is the project for your reference: Skin Cancer Detection using TensorFlow

Detecting COVID-19 through machine learning and deep learning has emerged as a pivotal tool in the global battle against the pandemic. Machine learning models, trained on diverse datasets of medical imaging such as X-rays and CT scans , exhibit remarkable proficiency in identifying patterns and anomalies associated with COVID-19 . Analyzing radiological images, these models discern subtle features that serve as indicators of the viral infection. The swift and accurate analysis by these algorithms facilitates rapid diagnoses, enabling prompt isolation and treatment of affected individuals. Utilizing the Xception model, a deep learning framework known for its efficiency in handling image data, the article outlines a method for analyzing chest X-ray images to differentiate between COVID-19 positive cases, viral pneumonia, and normal instances. The process involves preprocessing the images to fit the model’s input requirements, employing data augmentation techniques to enhance the dataset, and finally, training the Xception model to classify the images into the respective categories. Achieving an impressive accuracy rate on both training and validation sets, this approach exemplifies the potential of deep learning models like Xception in providing rapid and accurate diagnostic tools.

Here is a project for your reference: Detecting Covid-19 with Chest X-ray

“ Text Detection and Extraction using OpenCV and OCR” delves into the technical prowess of OpenCV and OCR for text detection and extraction, a methodology that can be adeptly applied to the healthcare sector for managing patient records. By employing OpenCV for image processing and Python -tesseract as a wrapper for Google’s Tesseract-OCR Engine , this approach automates the extraction of textual data from images, which is pivotal for digitizing and structuring health records. The process involves image preprocessing techniques such as color space conversion and thresholding, followed by contour detection to identify text blocks within images. This automated system not only streamlines the extraction of critical health information from various formats but also significantly reduces manual entry errors, thereby lightening the workload of healthcare professionals. The integration of machine learning, particularly through NLP algorithms, further refines the extraction and structuring of data from clinical notes, ensuring organized and standardized records. This synergy of machine learning technologies holds the promise of revolutionizing healthcare record management, offering a more accurate, real-time, and error-minimized approach to diagnosing and treating diseases.

Here is the project for your reference: Text Detection and Extraction Electronic Health Record

Top machine learning projects for Healthcare analytics, showcasing its prowess across various critical applications as detailed in this article. From the management of electronic health records to the diagnosis and prognosis of diseases, machine learning algorithms emerge as indispensable tools for healthcare providers. Using ML models the healthcare industry has emerged and a lot is yet to come. NLP and Computer Vision technology are widely used and is proofing their dominance in the healthcare industry. Harnessing the power of machine learning empowers healthcare providers to elevate patient outcomes, slash costs, and optimize operational efficiency.. Here we explored 10 machine learning projects for healthcare domain but I assure you there are many more projects and use cases in healthcare domain.

Machine Learning Projects for Healthcare – FAQ’s

How machine learning can help in predicting pandemics .

By harnessing data and real time updates, and web-based sources, the ML excel in foreseeing and predicting epidemic outbreaks. Although these examples showcase the current landscape of ML applications, the future holds the promise of even more sophisticated and revolutionary implementations in the realm of healthcare. As ML undergoes continuous evolution, we can expect paradigm-shifting developments that will transform lives, proactively prevent diseases.

Which machine learning models/Algorithms are used in healthcare?

Many models and algorithms are used in healthcare depending on the use case. From simpler Model like Logistic Regression to very complex model like RNN and LSTM can be used.

What is the use of NLP in Healthcare?

Since many of the healthcare records are unstructured textual data, so NLP can extract relevant information from clinical notes, transcripts, and other unstructured text, helping in the creation and updating of patient records. This improves the accuracy and efficiency of maintaining electronic health records.

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Analyze Healthcare Data with Python


  • September 9, 2020
  • Machine Learning

In this article, I will take you through how we can analyze Healthcare data with Python. The process of data analysis remains almost the same in most of the cases, but there are some domains which are very much categorical. One such domain is healthcare, so here you will learn how you can analyze healthcare data with Python.

The data I will be using in this article is from India. The data comes from NTR Vaidya Seva (or Arogya Seva) is the flagship health care program of the government of Andhra Pradesh, India, in which lower middle class and low-income citizens of the state of Andhra Pradesh can get free health care for many major illnesses and ailments. A similar program also exists in neighbouring Telangana state. You can easily download this dataset from here .

Also, Read – Why Python not as the First Programming Language?

Analyze Healthcare Data

Now, let’s import all the necessary libraries that we need to analyze the healthcare data with python:

Now let’s read the data and have a quick look at some initial rows from the data:

image for post

To have a quick look at the statistics we just need to use a describe function:

analyse healthcare data

Now to analyze this healthcare data in a better way we need to first look at how is the data distributed into columns. So let’s have a quick look at the columns of the dataset:

Data Exploration

value_counts () is a Pandas function that can be used to print data distributions (in the specified column). Let’s start by checking the gender statistics of the data:

It appears that there are duplicate values ​​in this column. Male and MALE are not two different sexes. We can substitute the column names to resolve this issue. I will also rename Male (Child) -> Boy and Female (Child) -> Girl for convenience:

Viewing the above distribution can be done easily using Pandas’ built-in plot feature:

image for post

Now let’s have a look at the age distribution by using the mean, median and mode:

Top 10 current ages of data. Do not hesitate to play by replacing 10 with the number of your choice:

Boxplots are commonly used to visualize a distribution when bar charts or point clouds are too difficult to understand:

analyse healthcare data

Analyze Healthcare Data Deeply

What if I wanted to analyze only the records relating to Krishna district? I should select a subset of data to continue. Fortunately, Pandas can help us do this too, in two steps: 1. Condition to be satisfied: data [‘DISTRICT_NAME’] == ‘Krishna’ 2. Insertion of the condition in the dataframe: data [data [‘DISTRICT_NAME’] == “Krishna”]:

Now, if we want the most common surgery, at the district level, this can be done by going through all the district names and selecting the data subset for that district:

We note that only two surgeries dominate all the districts: Dialysis (7 districts) Long bone fracture (6 districts).

Now, let’s have a look at the average claim amount district wise:

Now let’s look at the surgery statistics to analyze this healthcare data. I will use the Pandas GroupBy concept to collect statistics by grouping data by category of surgery. The Pandas groupby works similarly to the SQL command of the same name:

image for post

Cochlear implant surgery appears to be the most expensive surgery, costing an average of â‚ą 520,000. Prostheses cost â‚ą 1,200, the cheapest. The youngest age group is also that of cochlear implant surgery: 1.58 years, while neurology has an average age of 56 years.

Also, Read – Machine Learning project on Predicting Migration.

So this is how you can analyze healthcare data. Feel free to play by manipulating the parameters that I have used. I hope you liked this article on how to analyze healthcare data with Python. Feel free to ask your valuable questions in the comments section below. You can also follow me on Medium to learn every topic of Python and Machine Learning.



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Advancing Opportunities in Healthcare with Python-Based Machine Learning

3 cases that highlight how Python & machine learning could optimize medicine.

machine learning

It’s no secret that open source is driving significant machine learning (ML) innovation. At the heart of this trend is Python, which is widely considered the tool of choice for data science projects in general and ML initiatives in particular. This piece will look at the use of Python-based ML in healthcare in three specific areas.

Machine Learning in Healthcare and the Role of Python

ML has been a component of healthcare research since the 1970s, when it was first applied to tailoring antibiotic dosages for patients with infections. But with the increased volume of electronic health records (EHRs) and the explosion in genetic sequencing data, healthcare’s interest in ML is now at an all-time high.

According to McKinsey Research, big data and machine learning in pharma and medicine could generate a value of up to $100 billion annually. That’s based on better decision-making, optimized innovation, improved efficiency of research and clinical trials and the creation of new tools for physicians, consumers, insurers and regulators.

How does Python fit into this picture? It’s the go-to language for many developers, ranking as one of the most popular programming languages and used widely across various tech disciplines, from data engineers to web programmers. Python’s rising popularity also touches data science and ML.

And the emergence of open-source language automation presents tremendous opportunities in healthcare for Python-based ML. Python language builds can be completed in minutes with specific ML packages and be vetted for open-source security and licenses. What’s more, Python now features the bulk of all open-source ML and data engineering tools. Developers can use the language to efficiently build innovative solutions while ensuring that code is secure throughout the life cycle of the applications.

Use Case 1: Managing Hospitals and Patient Care

Hospitals and clinics are strongly resource-constrained, making cost control critical to sustainability. And ensuring medical staff, treatment and diagnostic facilities are scheduled efficiently is a large-scale optimization problem with many dimensions. Doctors need to identify patients who are not following their treatment protocol. Patients undergoing surgery need skilled staff to care for them, sometimes around the clock.

ML can play a role in all of this, from predictive inventory management to improved triage for emergency departments to patient surgery and care. That’s why industry analysts at Accenture estimate that by 2026, the ML health market could potentially save the U.S. healthcare economy $150 billion in annual savings.

Use Case 2: Diagnostics

Diagnostic errors have been linked to as much as 10% of all patient deaths and may also account for between 6% and 17% of all hospital complications. ML is one potential solution to diagnostic challenges, particularly when applied to image recognition in oncology (e.g., cancer tests) and pathology (e.g., bodily fluid tests). In addition, ML has also been shown to provide diagnostic insights when examining EHRs.

When it comes to correctly analyzing medical images, ML success rates of up to 92% sit just below senior clinician success rates of 96%. However, when ML diagnoses are vetted by pathologists, a 99.5% accuracy rate is achieved. And even more promising is the use of ML to provide diagnoses based on multiple images , such as computerized tomography (CT), magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) scans. The human brain has a hard time integrating these different views into a whole, but ML solutions were better be able to process each unique piece of information into a single diagnostic outcome.

Use Case 3: Disease Prognosis Prediction

Even the best medical practitioners struggle with predicting how diseases will progress. In fact, it’s more guesswork than science, especially when it comes to terminal illness. Existing solutions help improve patient treatment by better predicting disease prognosis. But these solutions are either too costly or too time-consuming to implement in a practical manner. What’s needed is a solution that provides better predictions more cheaply and quickly than existing methods.

With recent breakthroughs in artificial intelligence (AI), predictive prognosis solutions have turned to Python-based ML techniques for an answer. This type of solution has been used to predict the mortality of a patient within 12 months of a given date based on their existing EHR data.

Python was used to create a deep neural network (DNN) using Pytorch and Scikit-Learn in order to predict death dates for patients with terminal illnesses. Each patient’s EHR was put into the DNN, including current diagnosis, medical procedures and prescriptions. The DNN then provided results that allow doctors to bring in palliative care teams in a timelier manner.

Securing Innovation Through Python

As machine learning creates disruptive change across many industries, it only makes sense that it would be applied to the healthcare industry. With billions of dollars to be saved and better care to be delivered, the field is increasingly turning to ML. They are doing so most often via Python, the open-source language that many consider the best suited for ML initiatives. Developers can build solutions that benefit human health and well-being, confident in the knowledge that their work will be secure.

To optimize Python for ML in Healthcare, considering how to use, monitor and secure the language code or implement open-source language automation will be of benefit to companies to manage the risk and accelerate the innovation Python can deliver.

About Bart Copeland:

Bart Copeland is the CEO and president of ActiveState, which is reinventing Build Engineering with an enterprise platform that lets developers build, certify and resolve any open source language for any platform and any environment. ActiveState helps enterprises scale securely with open source languages and gives developers the kinds of tools they love to use.

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    His skill set includes Data Science, Machine Learning, Python, AWS SageMaker, TensorFlow, Deep Learning, Azure Machine Learning, ETL, Talend for Big Data, and Hadoop toolsets like Spark/Scala, Pyspark, Hive, and Kafka, among others. Report abuse. 3 Healthcare Projects for all levels.

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    At the heart of this trend is Python, which is widely considered the tool of choice for data science projects in general and ML initiatives in particular. This piece will look at the use of Python-based ML in healthcare in three specific areas. Machine Learning in Healthcare and the Role of Python.