Having brought this diabetes risk assessment project to life, I navigated the entire pipeline from raw data to a deployed, functional tool. I began by training and fine-tuning multiple machine learning models—including Logistic Regression, Random Forest, and XGBoost—to accurately predict diabetes risk based on key health indicators. Once the models were ready, I integrated them into an interactive Streamlit application, designing an intuitive interface that allows users to input their health parameters and receive instant, model-backed risk evaluations. I then uploaded the complete project to GitHub for version control and transparency, deployed the app via Streamlit Community Cloud for public access, and finally embedded the live application into a WordPress website to ensure it reaches a broader audience in an accessible and user-friendly format.
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 and evaluated multiple machine learning models for diabetes risk prediction
Developed an interactive web application using Streamlit
Deployed the application using Streamlit Community Cloud
Version-controlled the project code using GitHub
Embedded the live app into a WordPress site for broader accessibility
⚠️ 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.