EFFECT OF DISCRETIZATION METHOD ON THE DIAGNOSIS OF PARKINSON'S DISEASE

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dc.contributor.authorKaya, Ersin
dc.contributor.authorFindik, Oguz
dc.contributor.authorBabaoglu, Ismail
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T18:14:14Z
dc.date.available2020-03-26T18:14:14Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractImplementing different classification methods, this study analyzes the effect of discretization on the diagnosis of Parkinson's disease. Entropy-based discrelization method is used as the discretization method, and support vector machines, C4.5, k-nearest neighbors and Naive Bayes are used as the classification methods. The diagnosis of Parkinson's disease is implemented without using any preprocessing method. Afterwards, the Parkinson's disease dataset is classified after implementing entropy-based discretization on the dataset. Both results are compared, and it is observed that using discretization method increases the success of classification on the diagnosis of Parkinson's disease by 4.1% to 12.8%.en_US
dc.identifier.endpage4678en_US
dc.identifier.issn1349-4198en_US
dc.identifier.issn1349-418Xen_US
dc.identifier.issue8en_US
dc.identifier.startpage4669en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26356
dc.identifier.volume7en_US
dc.identifier.wosWOS:000293817400006en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherICIC INTERNATIONALen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROLen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectParkinson's diseaseen_US
dc.subjectEntropy-based discretization methoden_US
dc.subjectClassification methodsen_US
dc.titleEFFECT OF DISCRETIZATION METHOD ON THE DIAGNOSIS OF PARKINSON'S DISEASEen_US
dc.typeArticleen_US

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