Kaya, ErsinFindik, OguzBabaoglu, IsmailArslan, Ahmet2020-03-262020-03-2620111349-41981349-418Xhttps://hdl.handle.net/20.500.12395/26356Implementing 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%.eninfo:eu-repo/semantics/closedAccessParkinson's diseaseEntropy-based discretization methodClassification methodsArticle7846694678WOS:000293817400006N/A