Explainable Ensemble-Based Machine Learning Models for Detecting the Presence of Cirrhosis in Hepatitis C Patients

Hepatitis C is a liver infection caused by virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis gradually damages liver, often leading permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms illness for decades before developing Cirrhosis typically worsens point failure. with cirrhosis may also experience brain and nerve system damage, well gastrointestinal hemorrhage. Treatment focuses on preventing further progression disease. Detecting earlier therefore crucial avoiding complications. Machine learning (ML) has been shown be effective at providing precise accurate information use diagnosing several diseases. Despite this, studies so far used ML detect patients C. This study obtained dataset consisting 28 attributes 2038 Egyptian from Repository University California Irvine. Four algorithms were trained diagnose patients: Random Forest, Gradient Boosting Machine, an Extreme Boosting, Extra Trees model. The model outperformed other models achieving accuracy 96.92%, recall 94.00%, precision 99.81%, area under receiver operating characteristic curve 96% using only 16 features.