🏥 Medical Multi-Disease Risk Assessment System

An AI-powered, full-stack healthcare web application that predicts the risk of 8 major diseases using Machine Learning and provides intelligent medical document analysis powered by Large Language Models (LLMs).

---

✨ Key Features

Feature	Description
🩺 Multi-Disease Prediction	Risk assessment for Diabetes, Stroke, Parkinson's, Thyroid, Depression, Hepatitis, Heart Disease & Kidney Disease
📄 AI Medical Report Analysis	Upload medical PDFs for automatic extraction of diagnoses, medications & risk factors (Powered by Groq & Llama-3)
🤖 Intelligent AI Summaries	Natural-language health summaries explaining results to patients (Powered by Google Gemini API)
🔐 Secure Authentication	User registration & login with bcrypt-encrypted passwords
⚡ Modern UI/UX	Responsive, interactive interface built with Next.js & Tailwind CSS
🚀 High-Performance API	FastAPI backend with async request handling and Swagger docs

---

🔍 Supported Diseases & ML Models

Disease	Model Type
Diabetes	Scikit-learn
Stroke	Scikit-learn
Parkinson's	CatBoost
Thyroid	CatBoost
Depression	Scikit-learn
Hepatitis	Scikit-learn
Heart Disease	Scikit-learn
Chronic Kidney Disease	Scikit-learn

---

🛠️ Tech Stack

Layer	Technology
Frontend	Next.js, React, TypeScript, Tailwind CSS
Backend	FastAPI 0.135.1, Uvicorn 0.41.0, Python 3.12
Database	SQLite via SQLAlchemy 2.0.48
ML Models	Scikit-learn 1.3.2, CatBoost 1.2.10, LightGBM 4.6.0, XGBoost 3.2.0
AI & Cloud	Google Gemini API, Groq API (Llama-3.3-70B)

---

📦 Project Structure

Medical-Disease-Risk-Assessment-System/
├── Backend/
│   ├── main.py                # FastAPI entry point & API routes
│   ├── database.py            # SQLite database configuration
│   ├── models.py              # SQLAlchemy ORM models
│   ├── schemas.py             # Pydantic input schemas per disease
│   ├── predictor.py           # Disease prediction logic
│   ├── predict_utils.py       # Model loading utilities
│   ├── utils.py               # Password hashing helpers
│   ├── auth.py                # Authentication dependency
│   ├── models/                # Pre-trained ML model files (.pkl)
│   └── requirements.txt       # Python dependencies
├── Frontend/
│   ├── app/                   # Next.js App Router (pages & API routes)
│   ├── components/            # Reusable React components
│   ├── context/               # Auth context provider
│   └── package.json           # Node.js dependencies
└── README.md

---

🚀 Getting Started

Prerequisites

• Python 3.12+
• Node.js 18+
• Git

---

1️⃣ Clone the Repository

git clone https://github.com/rashedulalbab253/Medical-Multi-Disease-Risk-Assessment-System.git
cd Medical-Multi-Disease-Risk-Assessment-System

---

2️⃣ Backend Setup

cd Backend

# Create & activate virtual environment
py -3.12 -m venv venv

# Windows (PowerShell):
.\venv\Scripts\Activate.ps1
# Windows (CMD):
.\venv\Scripts\activate.bat
# macOS/Linux:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🔑 Configure Environment Variables

Create a .env file inside Backend/:

DATABASE_URL=sqlite:///./auth_db.db
SECRET_KEY=your_secret_key_here
ACCESS_TOKEN_EXPIRE_MINUTES=60
GEMINI_API_KEY=your_gemini_api_key_here       # https://aistudio.google.com/
GROQ_API_KEY=your_groq_api_key_here           # https://console.groq.com/

▶️ Start the Backend

.\venv\Scripts\python.exe -m uvicorn main:app --reload

Backend running at http://localhost:8000 | Swagger Docs at http://localhost:8000/docs

---

3️⃣ Frontend Setup

Open a new terminal:

cd Frontend

# Install dependencies
npm install --legacy-peer-deps

# Start the dev server
npm run dev

Create a .env.local file inside Frontend/:

NEXT_PUBLIC_API_URL=http://localhost:8000

Frontend running at http://localhost:3000

---

✅ Quick Start Summary

Step	Command	Location
1	git clone <repo-url>	—
2	cd Backend && py -3.12 -m venv venv	Project root
3	.\venv\Scripts\Activate.ps1	Backend/
4	pip install -r requirements.txt	Backend/
5	Create .env with API keys	Backend/
6	uvicorn main:app --reload	Backend/
7	cd Frontend && npm install --legacy-peer-deps	Project root
8	npm run dev	Frontend/

---

🧠 How to Use

1. Sign Up — Create a new account
2. Log In — Authenticate with your email & password
3. Select a Disease — Choose from 8 available risk assessments
4. Fill the Form — Enter health data (age, BMI, blood pressure, etc.)
5. Get Results — Receive instant High Risk / Low Risk prediction with AI-generated insights
6. PDF Analysis — Upload a medical PDF for AI-powered document analysis

---

⚙️ Troubleshooting

Problem	Solution
ModuleNotFoundError	Activate your venv and run pip install -r requirements.txt
Failed to fetch on frontend	Ensure backend is running & NEXT_PUBLIC_API_URL is set
scikit-learn unpickling errors	Must use scikit-learn==1.3.2 (model training version)
npm install errors	Use npm install --legacy-peer-deps
Database issues	SQLite DB is auto-created on first run

---

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to open a pull request.

📄 License

This project is intended for educational purposes.

---

Made with ❤️ by Rashedul Albab