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Stroke Prediction System Using AI & IoT

Medical Animation
Revolutionizing healthcare through real-time stroke prediction.

🚀 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.

✨ 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.

🛠️ 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

🏗️ System Architecture

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

📈 Data Sources

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

🔒 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.

🚧 Development Timeline

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

💻 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.

📄 License

This project is licensed under the MIT License.

📧 Contact

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