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Heart Disease Prediction and Monitoring System

Overview

The Heart Disease Prediction and Monitoring System is a mobile application developed as a final-year project using Python and the Flutter framework. This innovative application aims to detect heart disease in its early stages through machine learning algorithms. It leverages input parameters and data from smart wearables (such as health watches, smartwatches, and fitness trackers) to classify an individual’s heart health status.

Features

  • Early Detection of Heart Disease: Utilizes machine learning to analyze user data and predict potential heart disease. 🩺
  • Health Monitoring System: Automatically collects health data from smart wearables, providing continuous monitoring of the user's health status. 📊
  • Personalized Health Care Services: Offers tailored health recommendations and precautions based on the user's health data. 🌟
  • Proactive Alerts: Notifies users to take preventive actions if their health status indicates potential risks. ⚠️
  • User Insights: Provides users with detailed insights into their health status for better management of their heart health. 📈

Technologies Used

  • Frontend: Flutter
  • Backend: Python
  • Machine Learning: Scikit-learn , metplotlib , pandas
  • Fitbit API: For collecting data from wearables
  • AI Model API: Developed on FastAPI to fetch responses for heart disease predictions
  • Firebase: To store patient data and daily activities for tracking progress

Screenshots

demo

Installation

  1. Clone the repository: 
    git clone https://github.com/yourusername/heart-disease-prediction-monitoring.git
  2. Navigate to the project directory:
cd heart-disease-prediction-monitoring
  1. Install the necessary dependencies for Python:
pip install -r requirements.txt
  1. Set up Flutter on your local machine and ensure all dependencies are met. Run the application:
flutter run

Usage

Open the application on your mobile device. 📲 Connect your smart wearable to the app via the Fitbit API. Allow the app to collect data from the wearable. Follow the on-screen prompts to input any additional health parameters. View your health status, predictions, and personalized recommendations. Contributing We welcome contributions from the community! 🤝 To contribute:

Fork the repository.

  • Create a new branch (git checkout -b feature/YourFeature).
  • Make your changes and commit them (git commit -m 'Add your feature').
  • Push to the branch (git push origin feature/YourFeature).
  • Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Thanks to the mentors who supported this project. 🙏