Feature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classification

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dc.contributor.authorUzer, Mustafa Serter
dc.contributor.authorYilmaz, Nihat
dc.contributor.authorInan, Onur
dc.date.accessioned2020-03-26T18:41:58Z
dc.date.available2020-03-26T18:41:58Z
dc.date.issued2013
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThis paper offers a hybrid approach that uses the artificial bee colony (ABC) algorithm for feature selection and support vector machines for classification. The purpose of this paper is to test the effect of elimination of the unimportant and obsolete features of the datasets on the success of the classification, using the SVM classifier. The developed approach conventionally used in liver diseases and diabetes diagnostics, which are commonly observed and reduce the quality of life, is developed. For the diagnosis of these diseases, hepatitis, liver disorders and diabetes datasets from the UCI database were used, and the proposed system reached a classification accuracies of 94.92%, 74.81%, and 79.29%, respectively. For these datasets, the classification accuracies were obtained by the help of the 10-fold cross-validation method. The results show that the performance of the method is highly successful compared to other results attained and seems very promising for pattern recognition applications.en_US
dc.description.sponsorshipSelcuk University Scientific Research Projects CoordinatorshipSelcuk Universityen_US
dc.description.sponsorshipThe authors would like to thank Selcuk University Scientific Research Projects Coordinatorship for the support of this paper.en_US
dc.identifier.doi10.1155/2013/419187en_US
dc.identifier.issn1537-744Xen_US
dc.identifier.pmid23983632en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://dx.doi.org/10.1155/2013/419187
dc.identifier.urihttps://hdl.handle.net/20.500.12395/29518
dc.identifier.wosWOS:000322976600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherHINDAWI LTDen_US
dc.relation.ispartofSCIENTIFIC WORLD JOURNALen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleFeature Selection Method Based on Artificial Bee Colony Algorithm and Support Vector Machines for Medical Datasets Classificationen_US
dc.typeArticleen_US

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