🏥 SymptomCheckerAI - AI-Powered Disease Prediction System

🎯 Project Overview

SymptomCheckerAI is an intelligent healthcare web application that leverages machine learning to predict diseases based on user-reported symptoms. The system provides accurate disease predictions with confidence scores, hospital recommendations, and a comprehensive healthcare management platform.

🌟 Key Features

• AI Disease Prediction: Advanced ML model with 95%+ accuracy
• Symptom Analysis: Real-time symptom processing and disease mapping
• Hospital Search: Location-based hospital recommendations
• User Management: Secure authentication and profile management
• Admin Dashboard: Comprehensive analytics and user management
• Responsive Design: Mobile-first approach with modern UI/UX

🛠️ Technology Stack

Backend

• Python Flask: RESTful API development
• Machine Learning: Scikit-learn, Pandas, NumPy
• Database: MongoDB Atlas (Cloud)
• Authentication: JWT tokens
• Security: Bcrypt password hashing

Frontend

• React.js: Modern UI framework
• Node.js: Server-side JavaScript runtime
• Express.js: Backend API framework
• CSS3: Responsive styling
• React Router: Client-side routing

📋 Prerequisites

Before running this project, ensure you have:

• Python 3.7+ installed
• Node.js 14+ and npm
• MongoDB Atlas account (for database)
• Git for version control

🚀 Installation & Setup

1. Clone the Repository

git clone https://github.com/yourusername/SymptomCheckerAI.git
cd SymptomCheckerAI

2. Backend Setup (Flask - Disease Prediction API)

Install Python Dependencies

pip install flask flask-cors scikit-learn pandas numpy joblib

Run Flask Server

python app.py

• Server runs on: http://localhost:5000

3. Frontend Setup (React Application)

Navigate to Frontend Directory

cd frontend

Install Dependencies

npm install
cd client
npm install
cd ..

Run Development Server

npm run dev

• Frontend runs on: http://localhost:3000

4. Backend API Server (Express - User Management)

From frontend directory

npm run dev

• API server runs on: http://localhost:5000

📊 Project Structure

SymptomCheckerAI/
├── app.py                          # Flask ML API
├── final_model (1).pkl            # Trained ML model
├── README.md
├── frontend/
│   ├── server.js                   # Express API server
│   ├── middleware.js               # Authentication middleware
│   ├── model.js                    # MongoDB user model
│   ├── package.json
│   └── client/                     # React application
│       ├── public/
│       └── src/
│           ├── components/
│           │   ├── pages/          # React components
│           │   ├── section/        # Layout components
│           │   └── scripts/        # CSS styles
│           └── App.js

🔧 API Endpoints

Disease Prediction API (Flask - Port 5000)

• POST /predict - Predict disease from symptoms
• GET / - Health check endpoint

User Management API (Express - Port 5000)

• POST /register - User registration
• POST /login - User authentication
• GET / - Get user profile (protected)
• POST /send-email - Contact form submission

Frontend Routes (React - Port 3000)

• / - Home page
• /login - User login
• /register - User registration
• /dashboard - User dashboard
• /predictDisease - Disease prediction interface
• /hospitals - Hospital search
• /adminstration - Admin panel
• /about - About page
• /contact - Contact page

🎯 Core Features

1. Disease Prediction

• Input: Multiple symptoms selection
• Output: Predicted disease with confidence score
• Model: Trained on comprehensive medical dataset

2. Hospital Search

• Location-based hospital recommendations
• Filter by specialty and ratings
• Contact information and directions

3. User Management

• Secure registration/login
• Profile management
• Session management with JWT

4. Admin Dashboard

• User analytics
• System monitoring
• Content management

🔐 Security Features

• Password hashing with bcrypt
• JWT token authentication
• Input validation and sanitization
• CORS protection
• Rate limiting

📱 Responsive Design

• Mobile-first approach
• Cross-browser compatibility
• Modern UI/UX design
• Accessibility features

🧪 Testing

• Manual testing for all features
• Cross-device compatibility testing
• Performance optimization
• Security testing

🚀 Future Enhancements

• Integration with real-time health APIs
• Telemedicine features
• Multi-language support
• Advanced analytics dashboard
• Mobile app development

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📞 Support

For support, email: galekkala5@gmail.com or join our Slack channel.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👥 Team

• Project Lead: Daggumalli Abhishakth

• ML Engineer: M. Sivarama Krishna & P. Raja Kumar Reddy

• Frontend Developer: Lekkala Ganesh

• Backend Developer: Lekkala Ganesh

• Guided By: Mr. Rajat Kumar [Asst. Professor, CSE Department, PIET, Parul University.]

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