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

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dc.contributor.authorYağın, Fatma Hilal
dc.contributor.authorPınar, Abdulvahap
dc.date.accessioned2026-04-04T13:14:32Z
dc.date.available2026-04-04T13:14:32Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractThis 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.
dc.identifier.doi10.5455/medscience.2025.04.092
dc.identifier.endpage1317
dc.identifier.issn2147-0634
dc.identifier.issue4
dc.identifier.startpage1309
dc.identifier.trdizinid1369802
dc.identifier.urihttps://doi.org/10.5455/medscience.2025.04.092
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1369802
dc.identifier.urihttps://hdl.handle.net/11616/107299
dc.identifier.volume14
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofMedicine Science
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TR_20250329
dc.subjectTıbbi İnformatik
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectİnşaat Mühendisliği
dc.titleInvestigation of Hepatitis C diagnosis with machine learning and evaluation of clinical biomarkers with explainable artificial intelligence models
dc.typeArticle

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