cirrhosis

Competing-risks survival re-examination of the Mayo PBC cirrhosis benchmark (UCI Cirrhosis dataset): a leakage-free, reproducible pipeline benchmarking seven models against the Mayo clinical score.

A reproducible single-cell workflow for human liver fibrosis, MASH, and cirrhosis target discovery.

Machine learning project for predicting liver cirrhosis patient outcomes using clinical data and multi-class classification models.

A clinical-grade biophysical simulation of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) and Cirrhosis. Built with Spring Boot, Next.js, and Three.js, it translates FIB-4 laboratory parameters into real-time WebGL organ deformations and ECG electrophysiology models.

GastroCare_AI is an AI-powered gastroenterology and digestive health research platform. Content is compiled from published medical literature, clinical guidelines (ACG, BSG, EASL, AASLD, WHO), and research databases. It enables upload and analysis of reports. This open, collaborative system grows stronger with every user. Share it widely.

Machine learning project for predicting liver cirrhosis patient outcomes using clinical and laboratory data. Includes EDA, preprocessing, Random Forest classification, SMOTE balancing, hyperparameter tuning, permutation importance, and stakeholder-focused medical visualizations.