I developed and deployed a comprehensive heart disease prediction system from end to end. It all started with building and training multiple machine learning models—Logistic Regression, Random Forest, and XGBoost—using a curated medical dataset. After validating their performance, I saved the models and preprocessing tools for later use. I then created an interactive and user-friendly web application using Streamlit, which allows users to input patient parameters and receive real-time predictions from all three models. Once the app was ready, I uploaded the entire project to GitHub for version control and collaboration. The final step involved deploying the Streamlit app publicly and embedding it into my WordPress website, making the tool accessible online for demonstration and educational purposes.
This system is designed not as a diagnostic tool but as a potential assistive technology, and its future evolution must be guided by close collaboration with healthcare professionals to ensure it meets clinical needs and integrates safely into existing workflows.
Key Tasks Completed:
Trained multiple machine learning models (Logistic Regression, Random Forest, XGBoost) for heart disease prediction
Developed an interactive Streamlit web application for real-time predictions
Uploaded the project to GitHub for version control and sharing
Deployed the app on Streamlit’s public platform
Embedded the deployed app into a WordPress website
⚠️ Attention
If you see the message “This app has gone to sleep due to inactivity”, it simply means the service we use is hosted on a free platform that automatically goes to sleep when there hasn’t been any activity.
Just click “Wake it up.”
After it loads, you will see the application shown in the image below.