LIVER-CIRRHOSIS-STAGE-PREDICTION.MD
HEALTHCARE ML APP
Liver Cirrhosis Stage Prediction
A live machine learning application that predicts liver cirrhosis stage through a simple web interface built for accessible clinical-style risk assessment.
Overview
This project focuses on predicting liver cirrhosis stage through a browser-based machine learning application. The goal was to move beyond offline experimentation and make the prediction workflow accessible through a clean web interface that makes the system easier to test, understand, and demonstrate.
Problem
Healthcare prediction work often stays trapped inside notebooks or raw model files, which makes it harder for users to interact with the output in a practical way. I wanted to package a clinical-style classification workflow into something simple, usable, and easy to demonstrate.
Role
I worked on shaping the prediction workflow, connecting model behavior to a usable interface, and packaging the solution as a live deployment rather than a research-only artifact.
Implementation
- Built a live prediction flow that accepts user inputs and returns stage-oriented model output through a web interface.
- Organized the prediction experience so the model could be demonstrated through a simple browser workflow instead of local setup.
- Focused on usability and clarity so the project could communicate both the technical model and the product experience.
- Packaged the project for live access so the output could be shared and tested more easily.
Outcomes
- Converted a healthcare ML workflow into a live product-style experience.
- Made the prediction flow easier to demonstrate, evaluate, and discuss with non-technical users.
- Strengthened the bridge between machine learning experimentation and practical web delivery.