Bal, TayibeDirican, Emre2024-09-252024-09-2520241687-19792090-2387https://doi.org/10.1016/j.ajg.2024.03.003https://hdl.handle.net/20.500.12491/13188Background and study aims: The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in hepatitis C virus (HCV) antibody (anti-HCV) seropositive cases. Patients and Methods: This retrospective study was conducted between January 2012-January 2022 with 812 patients who were referred for anti-HCV positivity and were examined for HCV ribonucleic acid (HCV RNA). Models were constructed with 11 features with a predictor (presence and absence of viremia) to predict HCV viremia. To build an optimal model, this current study also examined and compared the three classifier data mining approaches: RF, SVM and XGBoost. Results: The highest performance was achieved with XGBoost (90%), which was followed by RF (89%), SVM Linear (85%) and SVM Radial (83%) algorithms, respectively. The four most important key features contributing to the models were: alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (ALB) and antiHCV levels, respectively, while ALB was replaced by the AGE only in the XGBoost model. Conclusion: This study has shown that XGBoost and RF based ML models, incorporating anti-HCV levels and routine laboratory tests (ALT, AST, ALB), and age are capable of providing HCV viremia diagnosis with 90% and 89% accuracy, respectively. These findings highlight the potential of ML models in the early diagnosis of HCV viremia, which may be helpful in optimizing HCV elimination programs.eninfo:eu-repo/semantics/closedAccessAlanine aminotransferaseHepatitis C virusMachine learningRandom forestXGBoostViremiaArticle10.1016/j.ajg.2024.03.003252223229387058152-s2.0-85192212971Q3WOS:001248486100001N/A