Having developed this liver disease prediction system from end to end, I began by training multiple machine learning models—Random Forest, SVM, and XGBoost—using a curated clinical dataset. I then engineered relevant features, preprocessed the data, and serialized the models along with artifacts like scalers and encoders. After validating model performance, I built an interactive and user-friendly Streamlit application to bring the prediction tool to life, integrating real-time input validation, dynamic risk visualization, and multi-model inference. I uploaded the complete project—including code, models, and documentation—to GitHub for version control and transparency, deployed the app via Streamlit Cloud for public access, and finally embedded it into my WordPress site to ensure it reaches a broader audience in an accessible and seamless manner.
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
Performed feature engineering and data preprocessing
Built an interactive Streamlit web application
Deployed the app using Streamlit Cloud
Uploaded the project to GitHub
Embedded the 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.