Bringing this breast cancer diagnosis assistant to life has been an incredibly rewarding technical journey. I started by building and fine-tuning a suite of machine learning models—Random Forest, XGBoost, and SVM—training them on a robust clinical dataset to recognize patterns in cell characteristics. The real challenge and fun part was then translating these complex models into something intuitive and accessible. I built a dynamic Streamlit application from the ground up, designing an interactive interface where users can adjust input sliders and immediately see how each factor influences the AI’s assessment. After rigorous testing, I deployed the live app using Streamlit Community Cloud, managed the codebase via GitHub, and seamlessly embedded the final, functioning application directly into my WordPress site, making the technology available to anyone with a web browser.
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 (Random Forest, XGBoost, SVM) for breast cancer classification
Developed an interactive Streamlit app with real-time input and visualization
Processed and scaled input features for model compatibility
Deployed the app using Streamlit Community Cloud
Uploaded all project files and models to GitHub
Embedded the live app into a WordPress website 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.