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

2026-06-24

AI Summary

This study addresses the challenge of early detection of cirrhosis in patients with hepatitis C by proposing a highly accurate and interpretable automated diagnostic approach based on ensemble learning. Leveraging 28 clinical features from 2,038 Egyptian patients, the work systematically evaluates several ensemble models—including Random Forest, Gradient Boosting Machine, XGBoost, and Extremely Randomized Trees—combined with feature selection and interpretability techniques. The optimal Extremely Randomized Trees model achieves exceptional performance using only 16 key features, yielding 96.92% accuracy, 94.00% recall, 99.81% precision, and an AUC of 96%, while maintaining strong clinical interpretability.

Abstract

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

Problem

• cirrhosis
• hepatitis C
• early detection
• machine learning
• diagnosis

Innovation

• Explainable AI
• Ensemble Learning
• Cirrhosis Detection
• Feature Selection
• Extra Trees