Comprehensive Health Prognosticator

This Web App was developed using the Python Flask Web Framework to predict various diseases. The models were trained on large datasets, and all the links for datasets and the Python notebooks used for model creation are provided below. The WebApp can predict the following diseases:

• Diabetes
• Breast Cancer
• Heart Disease
• Kidney Disease
• Liver Disease

Models with their Accuracy of Prediction

Disease	Accuracy
Diabetes	Machine Learning Model 98.25%
Breast Cancer	Machine Learning Model 98.25%
Heart Disease	Machine Learning Model 85.25%
Kidney Disease	Machine Learning Model 99%
Liver Disease	Machine Learning Model 78%

Installation and Usage

Steps to Run the WebApp Locally

1. Download the files in the repository:

   git clone https://github.com/yourusername/health-prognosticator.git

2. Navigate to the downloaded folder and install all the dependencies:

   cd health-prognosticator
   pip install -r requirements.txt

3. After successful installation of all the dependencies, run the following command:

   python app.py

Dataset Links

All the datasets were used from Kaggle:

• Diabetes Dataset
• Breast Cancer Dataset
• Heart Disease Dataset
• Kidney Disease Dataset
• Liver Disease Dataset

Project Documentation

This project is part of a software engineering course. Every principle of software engineering was followed, and documents are provided in the GitHub profile for each phase.

Contribution Guidelines

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch-name

3. Make your changes and commit them:

   git add .
   git commit -m "Description of the changes made"

4. Push to the branch:

   git push origin feature-branch-name

5. Open a pull request on GitHub.

Example Commands for Commits

1. Adding new features:

   git add .
   git commit -m "Added new feature: feature description"

2. Fixing bugs:

   git add .
   git commit -m "Fixed bug: bug description"

Acknowledgements

This project is supervised by prof Romi Banerjee. We thank her for her guidance and support.