Using Artificial Intelligence Methods for Detection of HCV-Caused Diseases

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dc.contributor.authorKoçak, Muhammed Tayyip
dc.contributor.authorKaya, Yılmaz
dc.contributor.authorKuncan, Fatma
dc.date.accessioned2024-12-24T19:17:06Z
dc.date.available2024-12-24T19:17:06Z
dc.date.issued2023
dc.departmentSiirt Üniversitesi
dc.description.abstractThe Hepatitis C Virus (HCV) can cause chronic diseases and even lead to more serious conditions such as cirrhosis and fibrosis. Early detection of HCV infection is crucial to prevent these outcomes. However, in the early stages of infection, when symptoms are not yet evident, patients rarely undergo HCV testing. This highlights the need for alternative materials to guide HCV testing for early detection of the disease. In this study, we investigate the use of artificial intelligence technology to determine the disease status of individuals using blood data. A total of 615 individuals were included in the study. Preprocessing, filtering, feature selection, and classification processes were applied to the blood data. The correlation method was used for feature selection, where the features with high correlation values were selected and given as input to five different classification algorithms. The results of the study showed that the K-Nearest Neighbor (KNN) algorithm achieved the best classification success for detecting HCV patients, with a rate of 99.1%. This research demonstrates that artificial intelligence technology can be an effective tool for early detection of HCV-related diseases. The results indicate that the KNN algorithm can provide clear information about hepatitis infection from different blood values. Future studies can explore the use of other AI techniques and expand the sample size to improve the accuracy of the model.
dc.identifier.doi10.30931/jetas.1216025
dc.identifier.endpage33
dc.identifier.issn2548-0391
dc.identifier.issue1
dc.identifier.startpage15
dc.identifier.trdizinid1175454
dc.identifier.urihttps://doi.org/10.30931/jetas.1216025
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1175454
dc.identifier.urihttps://hdl.handle.net/20.500.12604/4764
dc.identifier.volume8
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofJournal of Engineering Technology and Applied Sciences
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectMachine learning
dc.subjectclassification
dc.subjectHepatitis C virus
dc.subjectk-nearest neighbors
dc.subjectpreprocessing
dc.titleUsing Artificial Intelligence Methods for Detection of HCV-Caused Diseases
dc.typeArticle

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