Having successfully developed and deployed the Chronic Kidney Disease Detector, I’m excited to share the end-to-end process I completed. It began with training multiple machine learning models—Random Forest, Gradient Boosting, and XGBoost—using a curated medical dataset, followed by saving these models along with preprocessing artifacts like the scaler and label encoders. I then built an interactive and user-friendly Streamlit application that allows users to input their health parameters and receive real-time kidney disease risk assessments, complete with probability scores and actionable health recommendations. After thoroughly testing the app, I uploaded all the necessary files, including the trained models and script, to GitHub for version control and collaboration. The final step involved deploying the app via Streamlit Community Cloud to make it publicly accessible, and I seamlessly embedded it into my WordPress website, ensuring that this valuable health screening tool is available to a wider audience in an intuitive and professional 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 multiple machine learning models for kidney disease prediction
Built an interactive Streamlit application for real-time risk assessment
Uploaded all project files and models to GitHub
Deployed the app using Streamlit Community Cloud
Embedded the live 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.