A potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithms

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dc.authoridDIRICAN, EMRE/0000-0003-3550-1326
dc.contributor.authorBal, Tayibe
dc.contributor.authorDirican, Emre
dc.date.accessioned2024-09-18T19:52:33Z
dc.date.available2024-09-18T19:52:33Z
dc.date.issued2024
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractBackground 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.en_US
dc.identifier.doi10.1016/j.ajg.2024.03.003
dc.identifier.endpage229en_US
dc.identifier.issn1687-1979
dc.identifier.issn2090-2387
dc.identifier.issue2en_US
dc.identifier.pmid38705815en_US
dc.identifier.scopus2-s2.0-85192212971en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage223en_US
dc.identifier.urihttps://doi.org/10.1016/j.ajg.2024.03.003
dc.identifier.urihttps://hdl.handle.net/20.500.12483/7506
dc.identifier.volume25en_US
dc.identifier.wosWOS:001248486100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofArab Journal of Gastroenterologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlanine aminotransferaseen_US
dc.subjectHepatitis C virusen_US
dc.subjectMachine learningen_US
dc.subjectRandom foresten_US
dc.subjectXGBoosten_US
dc.subjectViremiaen_US
dc.titleA potential new way to facilitate HCV elimination: The prediction of viremia in anti-HCV seropositive patients using machine learning algorithmsen_US
dc.typeArticleen_US

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