Diabetes Prediction System

A full-stack web application for predicting diabetes risk based on various health parameters using machine learning.

Features

• User authentication (login/register)
• Profile management with image upload
• Diabetes risk prediction using machine learning
• BMI calculation
• Responsive design
• Google Sheets integration for data storage
• Secure password hashing
• File upload with size and type validation

Project Structure

├── server.js              # Main Express server
├── app.py                # Python Flask server for ML predictions
├── train_model.py        # Script for training the ML model
├── diabetes_model.joblib # Trained ML model
├── scaler.joblib        # Data scaler for preprocessing
├── diabetes.csv         # Training dataset
├── public/             # Static files
├── templates/          # HTML templates
└── requirements.txt    # Python dependencies

The Node.js server (server.js) handles user authentication, profile management, and serves static files and HTML templates. It communicates with the Python Flask server (app.py) to perform diabetes risk predictions.

Setup

1. Clone the repository
2. Install Node.js dependencies:

   npm install

3. Install Python dependencies:

   pip install -r requirements.txt

4. Create a .env file with the following variables:

   GOOGLE_SERVICE_ACCOUNT_EMAIL=your-service-account-email
   GOOGLE_PRIVATE_KEY=your-private-key
   GOOGLE_SHEETS_ID=your-sheets-id
   SESSION_SECRET=your-session-secret
   PORT=3000
   

5. Start the development servers:

   # Terminal 1 - Node.js server
   npm run dev
   
   # Terminal 2 - Python server
   python app.py

Deployment on Render

1. Create a free account on Render
2. Create two new Web Services:
   • One for Node.js backend
   • One for Python ML server
3. Connect your GitHub repository
4. Configure the services:
   • Node.js service:
     • Build Command: npm install
     • Start Command: npm start
   • Python service:
     • Build Command: pip install -r requirements.txt
     • Start Command: python app.py
5. Add environment variables in Render dashboard
6. Deploy!

Environment Variables

• GOOGLE_SERVICE_ACCOUNT_EMAIL: Google Cloud service account email
• GOOGLE_PRIVATE_KEY: Google Cloud private key
• GOOGLE_SHEETS_ID: Google Sheets ID for data storage
• SESSION_SECRET: Secret key for session management
• PORT: Server port (default: 3000)

Technologies Used

Backend

• Node.js
• Express.js
• Google Sheets API
• Python
• Flask
• Scikit-learn
• Joblib

Machine Learning Model

• The system uses a Random Forest Classifier model for diabetes prediction.
• The model was trained on the Pima Indians Diabetes Dataset (diabetes.csv).

Frontend

• HTML/CSS/JavaScript
• Bootstrap
• Multer for file uploads

Data Storage

• Google Sheets
• Local file system (for profile images)

Security

• bcrypt for password hashing
• express-session for session management
• Input validation and sanitization
• File upload restrictions

API Documentation

Authentication Endpoints

• POST /register - Register new user
• POST /login - User login
• GET /logout - User logout

User Endpoints

• GET /profile - Get user profile
• POST /profile/update - Update user profile
• POST /profile/upload - Upload profile image

Prediction Endpoints

• POST /predict - Get diabetes risk prediction

Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request