HealthGuard - Smart Community Health Monitoring System

A comprehensive web application for monitoring water-borne diseases in rural Northeast India, built for the Smart India Hackathon.

🌟 Features

Core Functionality

• AI Symptom Checker: Interactive chatbot for disease diagnosis
• Symptom Assessment Form: Structured form for detailed health evaluation
• Multi-language Support: English, Hindi, Assamese, and Bengali
• User Authentication: Secure login/signup system
• Health Dashboard: Real-time statistics and trends
• Water Quality Monitoring: Track water safety parameters
• Alert System: Real-time health warnings
• Educational Resources: Disease prevention and hygiene information
• User Profiles: Personal health history and reports
• AI Predictions: Outbreak risk assessment and forecasting

Technical Features

• Modern, responsive UI with minimal design
• Real-time data visualization with charts
• Secure backend API with rate limiting
• Local data storage with session management
• Cross-platform compatibility

🚀 Quick Start

Prerequisites

• Node.js (v14 or higher)
• npm or yarn package manager

Installation

1. Clone or download the project files

   cd jeevan_raksha_health

2. Install dependencies

   npm install

3. Start the application

   npm start

4. Access the application Open your browser and go to: http://localhost:3000

Alternative: Run without backend

If you want to run just the frontend:

1. Open index.html directly in your browser
2. The app will work with local storage (limited functionality)

📱 How to Use

For General Users

1. Browse without login: View dashboard, use symptom checker, access education
2. Create account: Sign up to save your health assessments
3. Check symptoms: Use either the chat interface or structured form
4. View reports: Access your personal health history (requires login)
5. Monitor alerts: Stay updated on health warnings in your area

For Health Workers

1. Data Collection: Report community health incidents
2. Water Quality: Submit water testing results
3. Alert Management: Monitor and respond to health alerts
4. Trend Analysis: View outbreak predictions and risk assessments

🛠️ Development

Project Structure

jeevan_raksha_health/
├── index.html          # Main application page
├── styles.css          # All styling and responsive design
├── script.js           # Frontend JavaScript functionality
├── server.js           # Backend API server
├── package.json        # Node.js dependencies
└── README.md          # This file

Key Technologies

• Frontend: HTML5, CSS3, JavaScript (ES6+)
• Backend: Node.js, Express.js
• Database: In-memory storage (SQLite recommended for production)
• Security: Helmet.js, CORS, Rate limiting
• UI Framework: Custom CSS with modern design principles

API Endpoints

• POST /api/auth/register - User registration
• POST /api/auth/login - User login
• POST /api/symptoms/analyze - Symptom analysis
• GET /api/reports/user/:userId - User health reports
• GET /api/dashboard/stats - Dashboard statistics
• GET /api/alerts - Health alerts
• POST /api/water-quality - Submit water quality data
• GET /api/predictions - AI predictions

🌐 Deployment

Local Development

npm run dev  # Uses nodemon for auto-restart

Production Deployment

1. Prepare for production:

   • Set up a proper database (SQLite/PostgreSQL)
   • Configure environment variables
   • Set up proper authentication with JWT
   • Enable HTTPS

2. Deploy to cloud platforms:

   • Heroku: git push heroku main
   • Netlify: Deploy frontend files
   • Vercel: Deploy with vercel command
   • AWS/Azure: Use respective deployment tools

Environment Variables

Create a .env file for production:

PORT=3000
NODE_ENV=production
JWT_SECRET=your-secret-key
DB_URL=your-database-url

🔧 Customization

Adding New Languages

1. Update the translations object in script.js
2. Add new language option in the language selector
3. Update the changeLanguage() function

Adding New Diseases

1. Update the waterBorneDiseases object in both script.js and server.js
2. Add corresponding symptoms and treatment information
3. Update the analysis algorithms

Styling Changes

• Modify styles.css for visual changes
• Update CSS custom properties for color themes
• Adjust responsive breakpoints as needed

📊 Data Management

User Data

• Stored locally in browser localStorage (development)
• Should be migrated to secure database for production
• Includes user profiles, health assessments, and reports

Health Data

• Symptom assessments and disease predictions
• Water quality measurements
• Alert notifications and responses
• Trend analysis and statistics

🔒 Security Considerations

Current Implementation

• Basic input validation
• CORS protection
• Rate limiting
• Helmet.js security headers

Production Recommendations

• Implement proper password hashing (bcrypt)
• Use JWT tokens for authentication
• Set up HTTPS/SSL certificates
• Add input sanitization
• Implement proper session management
• Regular security audits

🤝 Contributing

For Hackathon Team

1. Follow the existing code structure
2. Test all changes thoroughly
3. Update documentation for new features
4. Ensure mobile responsiveness

Code Style

• Use consistent indentation (2 spaces)
• Follow JavaScript ES6+ standards
• Comment complex functions
• Use meaningful variable names

📞 Support

Common Issues

1. Port already in use: Change PORT in package.json or kill existing process
2. Dependencies not installing: Clear npm cache and retry
3. Browser compatibility: Use modern browsers (Chrome, Firefox, Safari, Edge)

Troubleshooting

• Check browser console for JavaScript errors
• Verify Node.js version compatibility
• Ensure all dependencies are installed
• Check network connectivity for API calls

🏆 Hackathon Submission

Key Highlights

• Problem Solved: Water-borne disease monitoring in rural Northeast India
• Innovation: AI-powered symptom analysis and outbreak prediction
• Impact: Early warning system for vulnerable communities
• Technology: Modern web technologies with offline capabilities
• Scalability: Designed for easy deployment and scaling

Demo Script

1. Show the modern, intuitive interface
2. Demonstrate multi-language support
3. Use the AI symptom checker with sample symptoms
4. Show the structured symptom form
5. Display the comprehensive dashboard
6. Highlight the alert system
7. Show user authentication and personal reports
8. Demonstrate water quality monitoring
9. Present AI predictions and trends

Future Enhancements

• Mobile app development
• Integration with IoT sensors
• Machine learning model improvements
• Government health system integration
• SMS-based reporting for remote areas
• Offline functionality enhancement

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Built with ❤️ for Smart India Hackathon

Empowering communities through technology for better health outcomes