Stroke Prediction System Using AI & IoT

Revolutionizing healthcare through real-time stroke prediction.

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🚀 Project Overview

The Stroke Prediction System leverages AI and IoT wearables to provide real-time stroke risk assessment. By analyzing personal health data, wearables, and environmental inputs, the system issues early stroke alerts to help prevent severe outcomes.

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✨ Key Features

• Real-time Health Monitoring via wearables (heart rate, BP, ECG).
• AI-Driven Stroke Risk Prediction using patient history and lifestyle factors.
• Alert System for immediate high-risk detection.
• Environmental Data Integration to factor in pollution, temperature, etc.

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🛠️ Technology Stack

• Frontend: React.js
• Backend: Flask, Node.js
• AI & ML: TensorFlow, Scikit-learn
• Database: Firebase, MySQL
• IoT: Fitbit API, Google Fit
• Cloud: IBM Cloud, Vultr

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🏗️ System Architecture

Integration of patient data, wearables, and AI-driven prediction model.

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📈 Data Sources

• PhysioNet, MIMIC-IV: Patient medical data.
• Fitbit API, Google Fit: Real-time wearable data.
• OpenWeatherMap API: Environmental data.

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🔒 Security & Scalability

• HIPAA Compliance: End-to-end data encryption and access control.
• GDPR Compliance: Full user data privacy.
• Scalable Architecture: Built on cloud-native Kubernetes infrastructure.

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🚧 Development Timeline

1. Data Integration & AI Model – 4 weeks.
2. Frontend & IoT Integration – 3 weeks.
3. Testing & Deployment – 2 weeks.

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💻 Contribution

1. Fork the repository.
2. Create a branch: git checkout -b feature/new-feature.
3. Push changes: git push origin feature/new-feature.
4. Submit a PR.

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📄 License

This project is licensed under the MIT License.

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📧 Contact

• Project Lead: [Your Name]
• Email: [Your Email]