Using artificial intelligence methods for detection of HCV-Caused diseases

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dc.authorid0000-0003-2276-2658en_US
dc.contributor.authorKoçak, Muhammed Tayyip
dc.contributor.authorKaya, Yılmaz
dc.contributor.authorKuncan, Fatma
dc.date.accessioned2023-06-16T14:09:30Z
dc.date.available2023-06-16T14:09:30Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliği Bölümüen_US
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.en_US
dc.identifier.citationKoçak, M. T., Kaya, Y., & Kuncan, F. (2023). Using artificial intelligence methods for detection of HCV-Caused diseases. Journal of Engineering Technology and Applied Sciences, 8(1), pp. 15-33. https://doi.org/10.30931/jetas.1216025en_US
dc.identifier.doi10.30931/jetas.1216025en_US
dc.identifier.endpage33en_US
dc.identifier.issn2548-0391
dc.identifier.issue1en_US
dc.identifier.startpage15en_US
dc.identifier.urihttps://doi.org/10.30931/jetas.1216025
dc.identifier.urihttps://hdl.handle.net/20.500.13055/478
dc.identifier.volume8en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.institutionauthorKoçak, Muhammed Tayyip
dc.language.isoenen_US
dc.relation.ispartofJournal of Engineering Technology and Applied Sciencesen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectHepatitis C Virusen_US
dc.subjectK-Nearest Neighborsen_US
dc.subjectPreprocessingen_US
dc.subjectMachine Learningen_US
dc.subjectClassificationen_US
dc.titleUsing artificial intelligence methods for detection of HCV-Caused diseasesen_US
dc.typeArticleen_US
dspace.entity.typePublication

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