🏥 MediCare CDSS - Clinical Decision Support System

An AI-powered Clinical Decision Support System that analyzes medical lab reports and provides risk assessments for Diabetes, Cardiovascular, and Kidney health conditions.

🌟 Features

• PDF Lab Report Analysis: Upload medical lab reports in PDF format for automatic data extraction
• Multi-Panel Analysis: Simultaneous analysis of Diabetes, Cardiovascular, and Kidney health panels
• Hybrid AI System: Combines machine learning models with clinical rule-based systems
• AI Chatbot: Interactive AI assistant powered by Groq for explaining results
• Patient Profile Management: Manage multiple patient profiles with historical data
• Analysis History: Track and review past evaluations
• Dark/Light Theme: Modern, accessible UI with theme switching

🚀 Tech Stack

Frontend

• React 19 - UI framework
• React Router - Navigation
• Axios - HTTP client
• Material-UI - Component library
• Vite - Build tool

Backend

• FastAPI - Python web framework
• SQLAlchemy - ORM for database management
• XGBoost - Machine learning models
• SHAP - Model explainability
• Groq AI - LLM for chatbot
• PyPDF2 & pdfplumber - PDF processing
• JWT - Authentication

📋 Prerequisites

• Python 3.8+
• Node.js 16+
• npm or yarn
• Groq API Key (Get one here)

🛠️ Installation

1. Clone the Repository

git clone https://github.com/yourusername/medicare-cdss.git
cd medicare-cdss

2. Backend Setup

cd Backend

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Setup environment variables
copy .env.example .env
# Edit .env and add your GROQ_API_KEY

3. Frontend Setup

cd Frontend

# Install dependencies
npm install

# The frontend will connect to http://localhost:8000 by default

🏃 Running the Application

Start Backend Server

cd Backend
uvicorn app.main:app --reload --port 8000

Start Frontend Development Server

cd Frontend
npm run dev

Access the application at http://localhost:5173

📊 Health Panels

1. Diabetes Panel

• Parameters: HbA1c, FBS (Fasting Blood Sugar), PPBS (Post-Prandial Blood Sugar)
• Algorithm: XGBoost with Platt Calibration
• Features: 21 clinical features including BMI, age, lifestyle factors

2. Cardiovascular Panel

• Parameters: Blood Pressure (Systolic/Diastolic), Cholesterol, Heart Rate, BMI
• Algorithm: XGBoost with clinical feature engineering
• Risk Assessment: 10-year CHD (Coronary Heart Disease) risk

3. Kidney Panel

• Parameters: Serum Creatinine, Blood Urea (BUN), Age
• Algorithm: XGBoost with Standard Scaling
• Detection: Chronic Kidney Disease (CKD) classification

🔐 Authentication

The system uses JWT-based authentication:

• Register a new account
• Login to receive an access token
• Token is automatically included in API requests

📁 Project Structure

CDSS/
├── Backend/
│   ├── app/
│   │   ├── auth/          # Authentication logic
│   │   ├── database/      # Database configuration
│   │   ├── detection/     # PDF panel detection
│   │   ├── llm/           # Groq AI chatbot
│   │   ├── ml/            # ML predictors & explainers
│   │   ├── models/        # Database models
│   │   ├── pdf/           # PDF extraction
│   │   ├── routes/        # API endpoints
│   │   ├── Rules/         # Clinical rule engines
│   │   ├── schemas/       # Pydantic schemas
│   │   └── Services/      # Hybrid decision services
│   ├── Data_set/          # Training datasets
│   ├── ml_train/          # Model training scripts
│   ├── models/            # Trained ML models (.pkl)
│   └── sample_pdfs/       # Sample test PDFs
│
└── Frontend/
    └── src/
        ├── api/           # API client
        ├── components/    # React components
        ├── context/       # React context (Auth, Theme)
        └── pages/         # Page components

🌐 Deployment

This application can be deployed for free on Render. See our comprehensive guides:

• DEPLOYMENT_GUIDE.md - Step-by-step deployment instructions
• DEPLOYMENT_OPTIONS.md - Compare different hosting platforms
• PRE_DEPLOYMENT_CHECKLIST.md - Ensure you're ready to deploy

Quick Deploy on Render (Recommended)

1. Push your code to GitHub
2. Sign up at render.com
3. Create Web Service for Backend
4. Create Static Site for Frontend
5. Add environment variables
6. Deploy! 🚀

Estimated time: 30 minutes | Cost: Free

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📄 License

This project is licensed under the MIT License.

⚠️ Disclaimer

This system is designed as a clinical decision support tool and should NOT be used as a replacement for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical decisions.

📧 Contact

For questions or support, please open an issue on GitHub.

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Made with ❤️ for better healthcare