ML Ensembles Detect Cirrhosis in Hepatitis C Patients.

Hepatitis C can lead to severe liver damage, including cirrhosis, which often progresses to liver failure. Early detection of cirrhosis is crucial for effective treatment and preventing complications. This research explores the application of machine learning (ML) models to diagnose cirrhosis in Hepatitis C patients, an area previously underexplored by ML. The study utilized a dataset of 2038 Egyptian patients with 28 attributes. Four ensemble-based ML algorithms were trained: Random Forest, Gradient Boosting Machine, Extreme Gradient Boosting, and Extra Trees. The goal was to identify which model could most accurately detect cirrhosis. The Extra Trees model demonstrated superior performance, achieving an accuracy of 96.92%, a recall of 94.00%, and a precision of 99.81%. Notably, it achieved these results using only 16 of the original 28 features, highlighting its efficiency and potential for practical clinical application. This work underscores ML's capability to provide precise diagnostic information for critical medical conditions.