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https://github.com/s-araromi/cardiohealthassistant

A smart, AI-powered tool designed to assist in cardiovascular health management through personalized insights and reminders.
https://github.com/s-araromi/cardiohealthassistant

ai cardiovascular-health digital-health google-generative-ai health-tech machine-learning python streamlit streamlit-webapp

Last synced: 25 days ago
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A smart, AI-powered tool designed to assist in cardiovascular health management through personalized insights and reminders.

Host: GitHub
URL: https://github.com/s-araromi/cardiohealthassistant
Owner: s-araromi
Created: 2024-11-06T09:43:55.000Z (3 months ago)
Default Branch: main
Last Pushed: 2024-12-20T21:12:53.000Z (about 2 months ago)
Last Synced: 2024-12-20T21:25:02.482Z (about 2 months ago)
Topics: ai, cardiovascular-health, digital-health, google-generative-ai, health-tech, machine-learning, python, streamlit, streamlit-webapp
Language: Dart
Homepage:
Size: 370 KB
Stars: 1
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

# CardioHealthAssistant 💓🩺

## Overview
CardioHealthAssistant is an advanced, AI-powered health tracking and management application designed to provide comprehensive cardiovascular health insights, personalized recommendations, and proactive health monitoring.

![Project Banner](assets/banner.png)

## 🌟 Key Features

### 1. Health Metrics Tracking
- **Comprehensive Health Monitoring**
- Track critical cardiovascular health indicators
- Record and analyze:
- Cholesterol levels (Total, LDL, HDL)
- Blood sugar levels
- Blood pressure
- BMI calculations
- Exercise minutes
- Heart rate

### 2. AI-Powered Health Insights
- **Intelligent Analysis**
- Advanced machine learning algorithms
- Personalized health risk assessments
- Predictive health trend analysis
- **Gemini AI Integration**
- Natural language health consultations
- Contextual health recommendations
- Personalized wellness strategies

### 3. Notification & Reminder System
- **Multi-Channel Reminders**
- Medication adherence tracking
- Customizable reminder frequencies
- Email and SMS notifications
- Google Calendar event integration

### 4. Data Visualization
- **Interactive Health Dashboards**
- Time series trend analysis
- Distribution charts
- Correlation heatmaps
- Comparative box plots
- **Matplotlib, Seaborn, and Plotly Visualizations**

### 5. Mobile Companion App
- **Cross-Platform Support**
- Flutter-based mobile application
- iOS and Android compatibility
- Synchronized health tracking
- Real-time notifications

## 🛠 Technology Stack

### Backend
- **Language**: Python 3.12
- **Frameworks**:
- Streamlit
- Pandas
- NumPy
- **AI Integration**:
- Google Gemini AI
- Generative AI API

### Machine Learning
- Scikit-learn
- TensorFlow
- Predictive health modeling

### Mobile Development
- **Framework**: Flutter
- **Language**: Dart
- **Platform**: Cross-platform (iOS/Android)

### Notification Services
- Twilio SMS
- Google Calendar API
- SMTP Email

## 🚀 Quick Start

### Prerequisites
- Python 3.12+
- Flutter SDK
- Google Cloud Account
- Twilio Account (Optional)

### Installation

1. **Clone the Repository**
```bash
git clone https://github.com/s-araromi/CardioHealthAssistant.git
cd CardioHealthAssistant
```

2. **Setup Python Environment**
```bash
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```

3. **Configure Environment Variables**
- Copy `.env.example` to `.env`
- Fill in required API keys and credentials

4. **Run the Application**
```bash
streamlit run app.py
```

### Mobile App Setup
```bash
cd mobile_app
flutter pub get
flutter run
```

## 🔐 Security & Privacy

- End-to-end encryption
- Secure API key management
- HIPAA compliance considerations
- User data anonymization
- Secure authentication mechanisms

## 🤝 Contributing

1. Fork the repository
2. Create your feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

### Contribution Guidelines
- Follow PEP 8 style guide
- Write comprehensive unit tests
- Document new features and changes

## 📊 Project Roadmap

### Upcoming Features
- [ ] Advanced machine learning risk prediction
- [ ] Wearable device integration
- [ ] Telemedicine consultation booking
- [ ] Comprehensive health report generation
- [ ] Multi-language support

## 📱 Screenshots

![Dashboard](assets/dashboard.png)
![Mobile App](assets/mobile_app.png)

## 📄 License
MIT License

## 🏆 Acknowledgements
- Google Gemini AI
- Streamlit Community
- Flutter Team
- Open-source contributors

## 📞 Contact
**Sulaimon Araromi**
- Email: [email protected]
- LinkedIn: https://www.linkedin.com/in/sulaimon-araromi-395573151/
- Project Link: [https://github.com/s-araromi/CardioHealthAssistant](https://github.com/s-araromi/CardioHealthAssistant)

---

**Disclaimer**: This application is for informational purposes and should not replace professional medical advice. Always consult healthcare professionals for personalized medical guidance.