Investigation of Hepatitis C diagnosis with machine learning and evaluation of clinical biomarkers with explainable artificial intelligence models

OpenAlex İngilizce

This study aimed to evaluate the performance of machine learning (ML) models in the diagnosis of Hepatitis C Virus (HCV) patients and to identify clinical biomarkers using explainable artificial intelligence (XAI) approaches. Black box algorithms - Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were used in the study, and XAI methods - SHapley Additive Explanations (SHAP) and Accumulated Local Effects (ALE) were applied to increase the interpretability of the models. There were 615 patients in the dataset, and the output variable included various liver disorders including HCV. The results showed that RF and XGBoost models exhibited high performance with 93.75% and 92.38% accuracy rates, respectively. SHAP and ALE analyses revealed the importance and interactions of the factors (ALT, AST, bilirubin, albumin, age) underlying model decisions. This study demonstrates the potential of ML models in early diagnosis of HCV infection and how they can be integrated with XAI methods to make them more reliable in medical applications.