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.
- 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.
- Frontend: HTML, CSS, JavaScript
- Backend: Flask, Python
- Machine Learning: Scikit-learn, Pandas, NumPy
- Database: MySQL
- Model Serialization: Pickle
- Clone the Repository:
git clone https://github.com/asghar-rizvi/Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning.git
- Navigate to the Project Directory:
cd Health-Risk-Prediction-Platform-with-Flask-and-Machine-Learning - Install Required Dependencies: Ensure you have pip installed and use it to install the necessary packages: pip install -r requirements.txt
- Set Up the MySQL Database: Create a MySQL database and user. Update the config.py file with your database credentials.
- Migrate Database: Run the migration commands to set up the database tables: flask db upgrade
- Run the Flask Application: Start the development server : flask run
Health Predictions: Use the interactive forms on the prediction pages to receive instant health risk assessments.
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.
- Adding more health prediction models.
- Implementing real-time data visualization and analytics.
- Enhancing user interface for improved user experience.
For any inquiries or support, please reach out to: Asghar Qamber Rizvi Email: asgharqamberrozvi@gmail.com GitHub: asghar-rizvi