Adem, Kemal2025-01-212025-01-2120181308-5514https://dergipark.org.tr/tr/download/article-file/614668https://dergipark.org.tr/tr/pub/umagd/issue/42019/472881https://doi.org/10.29137/umagd.472881https://hdl.handle.net/20.500.12587/19979Late diagnosis of chronic kidney disease, adisease that has increased in recent years and threatens human life, may leadto dialysis or kidney failure. In this study, kNN, SVM, RBF and Random subspacedata mining methods were applied on the data set consisting of 400 samples and24 attributes taken from UCI for classification of chronic kidney disease with particleswarm optimization (PSO) based feature selection method. As a result of thestudy, the results of the application of each data mining method are comparedwith the resultant training and test results. As a result of the comparison, itwas seen that the method of PSO feature selection affects the classificationsuccess positively. Moreover, as a method of data mining, it has been seen thatthe random subspace method has higher accuracy rates than the other methods.eninfo:eu-repo/semantics/openAccessChronic kidney diseaseParticle Swarm OptimizationRandom SubspaceArticle13-1510.29137/umagd.472881472881