Health Risk Prediction Platform with Flask and Machine Learning

Project Overview

This project is a full-stack web application that predicts potential health risks, including heart attack risk, kidney disease likelihood, liver disease predictions, and diabetes forecasting. The application combines advanced machine learning models with a sleek and responsive frontend, providing users with accurate health predictions based on their input data.

Features

• Home Page: Introduction to the platform and its health prediction capabilities.
• User Authentication: Secure user registration and login using Flask and MySQL.
• Health Prediction Pages: Interactive forms for predicting heart attack risk, kidney disease, liver disease, and diabetes.
• Profile Management: Users can view and manage their profiles, securely storing their data.
• High Accuracy Models: Machine learning models trained on extensive datasets, achieving 98% accuracy.
• Backend Integration: Machine learning models are integrated into the Flask backend using Pickle for real-time predictions.

Technologies Used

• Frontend: HTML, CSS, JavaScript
• Backend: Flask, Python
• Machine Learning: Scikit-learn, Pandas, NumPy
• Database: MySQL
• Model Serialization: Pickle

Installation Instructions

1. Clone the Repository:

   git clone https://github.com/asghar-rizvi/Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning.git

2. Navigate to the Project Directory:

   cd Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning

3. Install Required Dependencies: Ensure you have pip installed and use it to install the necessary packages: pip install -r requirements.txt
4. Set Up the MySQL Database: Create a MySQL database and user. Update the config.py file with your database credentials.
5. Migrate Database: Run the migration commands to set up the database tables: flask db upgrade
6. Run the Flask Application: Start the development server : flask run

Usage

User Registration: Register a new account to access health prediction features.

Login: Securely log in to access your profile and prediction tools.

Health Predictions: Use the interactive forms on the prediction pages to receive instant health risk assessments.

Model Information

The platform employs four distinct machine learning models, each trained on health-related datasets. The models are evaluated for accuracy, with the top-performing models achieving up to 98% accuracy.

Future Enhancements

1. Adding more health prediction models.
2. Implementing real-time data visualization and analytics.
3. Enhancing user interface for improved user experience.

Contact Information

For any inquiries or support, please reach out to: Asghar Qamber Rizvi Email: asgharqamberrozvi@gmail.com GitHub: asghar-rizvi