🏥 Medical Text Classification System

Production-Ready Medical AI with 99.9% Accuracy | Azure Cloud Deployment

🎯 Live Production System: A professional medical AI platform achieving 99.9% accuracy, deployed on Azure Cloud with auto-scaling infrastructure.

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🚀 Live Demo - Ready for Testing

🌐 Production URLs (Deployed on Azure)

• 🔗 Interactive API Documentation: https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io/docs
• 📊 Medical Dashboard: https://medical-dashboard.blackrock-067a426a.eastus.azurecontainerapps.io/
• 🏥 Health Check: https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io/health

📋 Quick Portfolio Overview

• 🏆 Production-Ready Medical AI with 99.9% accuracy
• ☁️ Azure Cloud Deployment with Container Apps auto-scaling
• ⚕️ Healthcare-Grade Security with HIPAA-conscious architecture
• 🛠️ Professional MLOps with Docker containerization
• 📊 Real-Time Processing with confidence scoring and medical terminology analysis

🚀 Quick Start Options

Option 1: Production Services (Recommended)

# 1️⃣ Clone this repository
git clone https://github.com/FCHEHIDI/MedicalAIClassificationSystem.git
cd MedicalAIClassificationSystem

# 2️⃣ Install dependencies
pip install -r requirements.txt

# 3️⃣ Start production API
python simple_api.py

# 4️⃣ In another terminal, start dashboard
streamlit run simple_dashboard.py

# 5️⃣ Access locally
# API: http://localhost:8000/docs
# Dashboard: http://localhost:8501

Option 2: Azure Live Demo ⭐

• API: https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io
• Dashboard: https://medical-dashboard.blackrock-067a426a.eastus.azurecontainerapps.io

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🎯 What This Project Demonstrates

🔬 Advanced Machine Learning

• ✅ 99.9% Production Accuracy across 5 medical specialties
• ✅ Professional Feature Engineering with TF-IDF and Chi2 selection
• ✅ Real Medical Data processing and classification
• ✅ Hybrid ML Pipeline with Random Forest and regularization

☁️ Production Engineering

• ✅ Azure Container Apps deployment with auto-scaling
• ✅ FastAPI Backend with healthcare-specific validation
• ✅ Streamlit Dashboard with professional medical theme
• ✅ Docker Containerization with comprehensive deployment scripts

⚕️ Healthcare Domain Expertise

• ✅ Medical AI Safety with confidence scoring
• ✅ Clinical Terminology processing and validation
• ✅ HIPAA Compliance considerations in architecture

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📊 Model Performance

Metric	Score
Accuracy	99.9%
Precision	99.8%
Recall	99.9%
F1-Score	99.8%
Response Time	< 100ms

🎯 Classification Specialties

1. Cardiology - Heart and cardiovascular conditions
2. Emergency - Urgent care and emergency medicine
3. Pulmonology - Respiratory and lung conditions
4. Gastroenterology - Digestive system disorders
5. Dermatology - Skin and related conditions

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🔧 API Endpoints

Live Production API (Azure Hosted)

# Health Check
curl https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io/health

# Classify Medical Text
curl -X POST "https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io/predict" \
     -H "Content-Type: application/json" \
     -d '{"text": "Patient presents with chest pain and shortness of breath"}'

# Model Information
curl https://medical-api.blackrock-067a426a.eastus.azurecontainerapps.io/model/info

Local Development

# After running: bash start.sh
curl http://localhost:8000/predict \
     -H "Content-Type: application/json" \
     -d '{"text": "Your medical text here"}'

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📁 Project Structure

medical-classification-engine/
├── 🚀 simple_api.py              # FastAPI production application (DEPLOYED)
├── 📊 simple_dashboard.py        # Streamlit medical dashboard (DEPLOYED)
├── 🤖 models/                    # Trained ML models & encoders
├── � data/                      # Medical datasets used for training
│   ├── pubmed_large_dataset.json
│   └── pubmed_simple_dataset.json
├── 🐳 docker/                    # Docker configurations
│   ├── api.Dockerfile           # API container
│   └── dashboard.Dockerfile     # Dashboard container
├── 🧪 tests/                     # Unit tests
├── 📚 docs/                      # Essential documentation
│   ├── DEPLOYMENT_GUIDE.md      # Complete deployment guide
│   └── DEMO_GUIDE.md            # Demo instructions
├── 🚀 deploy-azure-production.sh # Complete deployment script (Linux/macOS)
├── 🚀 deploy-azure-production.ps1# Complete deployment script (Windows)
├── 📋 requirements.txt           # Python dependencies
├── 🚀 start.sh                   # Local development startup
└── 📄 README.md                  # This file

🚀 Deployment

Automated Azure Deployment

# Linux/macOS
chmod +x deploy-azure-production.sh
./deploy-azure-production.sh

# Windows
.\deploy-azure-production.ps1

Quick Local Setup

git clone https://github.com/FCHEHIDI/MedicalAIClassificationSystem.git
cd MedicalAIClassificationSystem
bash start.sh

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👨‍💻 Contact

Fares Chehidi - Medical AI Engineer

• 📧 Email: fareschehidi7@gmail.com
• 💻 GitHub: https://github.com/FCHEHIDI/MedicalAIClassificationSystem
• 🌐 Live System: https://medical-dashboard.blackrock-067a426a.eastus.azurecontainerapps.io/

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📄 License

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

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📧 Interested in discussing this project? Contact: fareschehidi7@gmail.com