About the Heart Disease Prediction Web App
Project Overview
Welcome to the Heart Disease Prediction Web App, a powerful tool designed to help you assess the risk of heart disease based on a set of clinical parameters. This application leverages advanced machine learning models to provide accurate predictions and valuable insights into heart health.
How It Works
- Input Data: You can provide clinical parameters such as age, sex, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, ECG results, and other relevant features.
- Prediction: Once you submit the data, the application uses a trained machine learning model to predict the likelihood of heart disease.
- Results: The prediction is displayed on the web page and also sent to the email address you provide. Additionally, all prediction data and results are stored in a cloud database for further analysis.
Features
- User-Friendly Interface: Easily enter patient data using intuitive input forms.
- Email Notifications: Receive prediction results directly to your email.
- Cloud Storage: All data and results are securely stored in a cloud database.
- Scalable Deployment: The app is deployed on Azure, ensuring high availability, security and reliability.
Technology Stack
The application is built using:
- Flask: A lightweight web framework to handle web requests and responses.
- Scikit-learn, XGBoost, LightGBM: Advanced machine learning libraries for model training and evaluation.
- Azure: Cloud platforms for deployment and secure data storage.
- Bootstrap and CSS: For a responsive and visually appealing user interface.
Future Enhancements
The plan to further enhance this application by incorporating automated model training pipelines, integrating with model registries, and exploring additional advanced features. Stay tuned for updates!
Contact Us
Thank you for using the Heart Disease Prediction Web App. If you have any questions or feedback, please feel free to reach out.