CardioSense.AI is an advanced AI-powered heart disease prediction system designed to provide early risk assessment based on key health indicators. Leveraging XGBoost and an AI chatbot, it delivers instant Yes/No predictions, personalized risk scores, and actionable health recommendations to empower individuals in their healthcare journey.
π¬ Theme: Healthcare
π Problem Statement: Early disease prediction for heart health
π‘ Solution: AI-based risk prediction with user-friendly reports and chatbot assistance
| Feature | Benefit |
|---|---|
| β Early Detection | Helps in preventing severe heart complications |
| β Affordable & Accessible | Eliminates the need for costly medical tests |
| β AI Chatbot Support | 24/7 assistance for health-related queries π€ |
| β Downloadable Health Reports | Easy sharing with medical professionals π |
| β Scalability | Seamless integration with hospitals & telemedicine services π₯ |
- π₯ AI-driven heart disease risk prediction using XGBoost with high accuracy
- β Instant Diagnosis: Yes/No result with an explanatory risk percentage
- π Personalized Health Insights: Tailored recommendations based on user data
- π€ AI Chatbot Support: 24/7 user assistance for health queries
- π Downloadable PDF Reports: Easily share results with healthcare professionals
- π‘ Cloud-Based & Scalable: Ready for integration into telemedicine platforms
| Step | Description |
|---|---|
| 1οΈβ£ User Input | Users enter health parameters (age, BMI, smoking status, glucose levels, etc.) |
| 2οΈβ£ Data Preprocessing & Feature Selection | Data is cleaned, and key features are selected |
| 3οΈβ£ XGBoost Model Processing | AI model predicts heart disease risk |
| 4οΈβ£ Prediction Output | Displays Yes/No risk level & risk percentage |
| 5οΈβ£ AI Chatbot & Report Generation | Provides real-time guidance & downloadable reports π |
π₯ Demo Video: Watch Now
π Dataset: Kaggle Dataset
π Project Report & Video: Google Drive
| Impact Category | Expected Improvement |
|---|---|
| π Reduction in Heart Disease Mortality | 25% |
| π Increase in Preventive Checkups | 50% |
| π° Healthcare Cost Savings | 30-40% |
| Technology | Purpose |
|---|---|
| π Python | AI model development |
| β‘ XGBoost | Machine Learning algorithm |
| π Flask | Web Application backend |
| π Streamlit | Interactive visualization |
| π€ AI Chatbot | Real-time user assistance |
| βοΈ Cloud Deployment | Scalability and accessibility |
πΉ Integration with Wearable Devices (Apple Watch, Fitbit)
πΉ Mobile App Development for real-time tracking π±
πΉ Expansion to other disease predictions using AI
πΉ Multi-language Support for global accessibility π
| Name | Role |
|---|---|
| π¨βπ Srinjoy Pramanik | Backend Development(team lead) |
| π¨βπ Soumyajit Dutta | Backend & Data Processing |
| π¨βπ Arpan Chowdhury | Frontend & UI/UX |
| π¨βπ Syed Md. Musharraf | Chatbot & Integration ML |
| π¨βπ Rudrasish Dutta | ML Expert |
# Clone the repository $ git clone https://github.com/yourusername/CardioSense.AI.git # Navigate to the project folder $ cd CardioSense.AI # Install dependencies $ pip install -r requirements.txt # Run the application $ python app.py
We welcome contributions! π
- Fork the repository π΄
- Create a branch for your feature/fix π±
- Commit your changes with a clear message β
- Submit a pull request π
Let's work together to revolutionize heart disease prediction! β€οΈ
π© Have feedback? Open an issue or drop us a message!
π
Developed for Healthcare Innovation Challenges
π Hackathon Participation & Awards
ποΈ Recognized for AI-driven Predictive Healthcare Solutions
π MIT License - Feel free to use, modify, and enhance CardioSense.AI π
π§ Email us at: teamdebuggers@email.com
π If you like this project, give it a star β on GitHub!
π Let's make heart disease prediction accessible for everyone! π