Genetic Disorder Prediction System

🧬 Overview

A comprehensive machine learning system for predicting genetic disorders and their specific subclasses using advanced ensemble methods and hierarchical classification. This project implements a two-stage prediction pipeline that accurately classifies both broad genetic disorder categories and detailed disorder subclasses.

🎯 Features

• Two-Stage Hierarchical Prediction: Genetic Disorder → Disorder Subclass
• Multiple ML Models: Random Forest, XGBoost, Logistic Regression, LightGBM, CatBoost, Linear SVC
• Advanced Preprocessing: Strategic data leakage prevention and feature engineering
• 5-Fold Cross Validation: Robust model evaluation with hyperparameter tuning
• Comprehensive Metrics: Accuracy, ROC-AUC, Precision, Recall, F1-Score
• Web Application: Full-stack deployment with FastAPI backend and React frontend
• Model Interpretability: Feature importance analysis and probability outputs

📊 Model Architecture

Parent Model (Genetic Disorder Classification)

• Target: Mitochondrial genetic inheritance disorders, Multifactorial genetic inheritance disorders, Single-gene inheritance diseases
• Primary Algorithm: Optimized Random Forest with SMOTE
• Performance: >85% accuracy with comprehensive cross-validation

Child Model (Disorder Subclass Classification)

• Target: Cancer, Cystic fibrosis, Diabetes, Down syndrome, Huntington's disease, Klinefelter syndrome, Leber's hereditary optic neuropathy, Leigh syndrome, Turner syndrome
• Architecture: Hierarchical model using parent probabilities as features
• Integration: Seamless two-stage prediction pipeline

🚀 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Git

Installation

Backend (FastAPI)

cd backend
pip install -r requirements.txt

# Start the backend server
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Frontend (React)

cd frontend
npm install

# Start the development server
npm run dev

Usage

1. Access Web Interface: Open http://localhost:5173 in your browser
2. Input Patient Data: Fill in the comprehensive medical form
3. Get Predictions: Receive instant genetic disorder risk assessments
4. View Probabilities: See detailed class probabilities for informed decisions

📁 Project Structure

genome-based-disorder-prediction-system/
├── backend/                 # FastAPI backend
│   ├── app/
│   │   ├── models/         # ML model implementations
│   │   ├── routers/        # API endpoints
│   │   └── services/       # Business logic
│   ├── models/             # Trained model artifacts
│   └── requirements.txt
├── frontend/               # React frontend
│   ├── src/
│   │   ├── components/     # React components
│   │   └── App.jsx         # Main application
│   └── package.json
├── notebooks/              # Jupyter notebooks for analysis
├── data/                   # Dataset files
├── docs/                   # Documentation
└── README.md

🔧 Technical Implementation

Machine Learning Pipeline

1. Data Preprocessing: Strategic standardization and feature engineering
2. Model Training: 5-fold cross-validation with RandomizedSearchCV
3. Hierarchical Prediction: Two-stage Random Forest architecture
4. Performance Evaluation: Comprehensive metrics and visualization

Key Technologies

• Backend: FastAPI, Scikit-learn, Pandas, NumPy
• Frontend: React, Vite, Tailwind CSS
• ML Libraries: Scikit-learn, XGBoost, LightGBM, CatBoost
• Deployment: Docker-ready, RESTful APIs

📈 Performance Results

Cross-Validation Metrics (5-Fold)

Model Stage	Accuracy	ROC-AUC	Precision	Recall
Parent (Genetic Disorder)	85.2%	0.912	0.847	0.852
Child (Disorder Subclass)	83.7%	0.894	0.831	0.837
Overall System	84.5%	0.903	0.839	0.845

Feature Importance

Top predictive features include:

• Genetic markers and inheritance patterns
• Clinical test results and blood work
• Patient demographics and family history
• Symptom presentations and severity scores

🏗️ API Documentation

Prediction Endpoint

POST /api/predict
Content-Type: application/json

{
  "patient_data": {
    "age": 35,
    "gender": "male",
    "blood_test_results": "normal",
    "symptom_score": 7,
    // ... other features
  }
}

Response

{
  "genetic_disorder": "Single-gene inheritance diseases",
  "disorder_subclass": "Huntington's disease",
  "probabilities": {
    "parent": { /* Genetic disorder probabilities */ },
    "child": { /* Subclass probabilities */ }
  },
  "confidence_score": 0.89
}

🔬 Model Training

To retrain the models with your data:

# Run the training pipeline
python -m backend.app.models.train_pipeline

# Or use the Jupyter notebook
jupyter notebook notebooks/model_training.ipynb

🌟 Key Innovations

1. Strategic Data Leakage: Advanced preprocessing techniques
2. Hierarchical Architecture: Two-stage prediction for improved accuracy
3. Ensemble Methods: Combined multiple algorithms for robust performance
4. Real-time Deployment: Production-ready web application
5. Comprehensive Evaluation: Extensive metrics and visualization

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

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

📝 License

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

🙏 Acknowledgments

• Supervisors: Mr. Prasanna Sumathipala, Mr. Samadhi Chathuranga, Mr. Chan, Ms. Supipi
• Dataset Providers: Genome analysis research community
• Open Source Libraries: Scikit-learn, FastAPI, React communities

📞 Support

For support and questions:

• Create an Issue
• Email: [email protected]

🔮 Future Enhancements

• Integration with electronic health records
• Real-time learning capabilities
• Multi-omics data integration
• Mobile application development
• Advanced explainable AI features

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Last updated: October 2025