Diabetes Prediction Platform 🏥

📊 Project Overview

A comprehensive machine learning web application for diabetes prediction, featuring real-time model evaluation and interactive performance visualization. This platform combines modern ML techniques with an intuitive interface to help analyze diabetes risk factors and make predictions.

✨ Key Features

🤖 Advanced Machine Learning Models

Support Vector Machine (SVM)

• Multi-kernel architecture (RBF, Linear, Polynomial, Sigmoid)
• Dynamic regularization parameter optimization
• Hyperplane visualization for decision boundary analysis
• Kernel performance comparison analytics

K-Nearest Neighbors (KNN)

• Adaptive neighbor selection algorithm
• Distance-weighted prediction system
• Automated feature space normalization
• K-value optimization through cross-validation

Logistic Regression

• L1/L2 regularization support
• Automated learning rate optimization
• Feature importance ranking
• Probability calibration

Decision Tree

• Dynamic depth optimization
• Automated pruning mechanisms
• Feature importance visualization
• Tree structure visualization

📈 Advanced Analytics

• Real-time Performance Metrics

  • ROC-AUC curve analysis
  • Precision-Recall curve visualization
  • Confusion matrix heat mapping
  • Cross-validation performance tracking

• Feature Analysis

  • SHAP (SHapley Additive exPlanations) values
  • Partial dependence plots
  • Feature importance rankings
  • Correlation analysis dashboard

🎯 Intelligent Preprocessing

• Data Cleaning

  • Automated outlier detection and handling
  • Missing value imputation using KNN
  • Feature scaling optimization
  • Data integrity validation

• Feature Engineering

  • Automated feature selection
  • Polynomial feature generation
  • Interaction term analysis
  • Dimension reduction techniques

🚀 Technical Architecture

graph TD
    A[Web Interface] --> B[Flask API Layer]
    B --> C[Model Manager]
    C --> D[Data Preprocessor]
    C --> E[Model Registry]
    D --> F[Feature Engineer]
    E --> G[Model Evaluator]
    G --> H[Performance Analytics]

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🛠️ Technology Stack

Backend Infrastructure

• Python 3.9+ - Core programming language
• Flask - Web framework with RESTful capabilities
• Scikit-learn - Machine learning framework
• Pandas & NumPy - Data processing and numerical computations
• Redis - Model caching and performance optimization
• SQLAlchemy - Database ORM for model versioning

Frontend Technologies

• HTML5/CSS3 - Modern web standards
• JavaScript (ES6+) - Dynamic functionality
• Bootstrap 5 - Responsive design framework
• Chart.js - Interactive visualizations
• D3.js - Advanced data visualizations

Development Tools

• Docker - Containerization
• Git - Version control
• Black - Code formatting
• Pytest - Testing framework
• GitHub Actions - CI/CD pipeline

📦 Installation

Using Docker (Recommended)

# Clone the repository
git clone https://github.com/parisa-roozgarian/diabetes-prediction.git

# Navigate to project directory
cd diabetes-prediction

# Build and run with Docker
docker-compose up --build

Manual Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Unix
.\venv\Scripts\activate   # Windows

# Install dependencies
pip install -r requirements.txt

# Run application
python run.py

💻 Usage Guide

Model Training and Evaluation

from diabetes_predictor import ModelManager

# Initialize model manager
model_manager = ModelManager()

# Train and evaluate models
results = model_manager.train_evaluate(
    model_type='svm',
    params={
        'kernel': 'rbf',
        'C': 1.0
    }
)

# Access performance metrics
print(results.metrics)

API Integration

import requests

# Make predictions
response = requests.post(
    'http://localhost:5000/api/predict',
    json={
        'glucose': 155,
        'blood_pressure': 70,
        'bmi': 25.1,
        # Additional features...
    }
)

prediction = response.json()

🔍 Model Performance

Model	Accuracy	F1 Score	Precision	Recall	ROC-AUC
SVM (RBF)	0.89	0.88	0.87	0.89	0.91
KNN	0.85	0.84	0.86	0.83	0.88
Logistic Regression	0.86	0.85	0.84	0.87	0.89
Decision Tree	0.83	0.82	0.81	0.84	0.85

🧪 Testing

# Run unit tests
pytest tests/unit

# Run integration tests
pytest tests/integration

# Generate coverage report
pytest --cov=diabetes_predictor tests/

📚 API Documentation

Comprehensive API documentation is available at /docs endpoint after running the application. The documentation includes:

• Endpoint specifications
• Request/response schemas
• Authentication requirements
• Rate limiting information
• Example requests

🔐 Security Features

• JWT-based authentication
• Rate limiting
• Input validation
• CORS protection
• Data encryption
• Audit logging

🤝 Contributing

We welcome contributions! Please follow these steps:

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

See CONTRIBUTING.md for detailed guidelines.

📋 Project Roadmap

• Basic model implementation
• API development
• Frontend dashboard

📄 License

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

👩‍💻 Author

Parisa Roozgarian

• LinkedIn: Parisa Roozgarian
• GitHub: @parisa-roozgarian
• Portfolio: www.parisaroozgarian.com

🙏 Acknowledgments

• Pima Indians Diabetes Database for the dataset
• scikit-learn team for the excellent ML framework
• Flask team for the robust web framework
• Open source community for various tools and libraries

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Made with ❤️ by Parisa Roozgarian