Modern Diabetes Prediction Web Application

A modern, interactive web application for predicting diabetes risk based on health parameters. The application features a clean, responsive UI with dark/light mode, interactive visualizations, and local storage for prediction history.

Features

• Interactive UI: Modern design with animations and visual feedback
• Dark/Light Mode: Toggle between dark and light themes
• Responsive Design: Works on desktop, tablet, and mobile devices
• Health Parameter Visualization: Visual indicators for blood glucose levels
• Result Charts: Visual representation of your health parameters
• BMI Calculator: Calculate your BMI with height and weight
• HbA1c Calculator: Estimate HbA1c from average blood glucose
• Prediction History: Save and retrieve your prediction history
• Health Resources: Information about diabetes and prevention

Technology Stack

• Backend: Flask (Python)
• Frontend: HTML5, CSS3, JavaScript
• UI Framework: Bootstrap 5
• Icons: Font Awesome
• Charts: Chart.js
• Containerization: Docker

Running the Application

Using Docker (Recommended)

1. Make sure you have Docker and Docker Compose installed on your system

2. Clone the repository:

   git clone <repository-url>
   cd diabetes_prediction
   

3. Build and run the container using Docker Compose:

   docker-compose up -d
   

4. Access the application in your browser:

   http://localhost:5000
   

5. To stop the application:

   docker-compose down
   

Manual Setup

1. Make sure you have Python 3.9+ installed

2. Clone the repository:

   git clone <repository-url>
   cd diabetes_prediction
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

4. Run the application:

   python app.py
   

5. Access the application in your browser:

   http://localhost:5000
   

Environment Variables

The application can be configured using the following environment variables:

• PORT: The port to run the application on (default: 5000)
• FLASK_DEBUG: Enable debug mode (default: False)
• FLASK_APP: The Flask application file (default: app.py)

Model Information

The diabetes prediction model is trained using scikit-learn and considers the following parameters:

• Age
• BMI (Body Mass Index)
• HbA1c Level
• Blood Glucose Level
• Gender
• Smoking History

Database

The application uses SQLite to store prediction history. The database file is created in the data directory.

License

MIT License

Contributors

• Subhojit Pramanicksubhojitpramanick.allwork@gmail.com

Acknowledgements

• Bootstrap for the UI framework
• Chart.js for data visualization
• Font Awesome for icons
• Flask team for the web framework