Diagnosis of Chronic Kidney Disease using Random Subspace Method with Particle Swarm Optimization

Abstract

Late diagnosis of chronic kidney disease, a disease that has increased in recent years and threatens human life, may lead to dialysis or kidney failure. In this study, kNN, SVM, RBF and Random subspace data mining methods were applied on the data set consisting of 400 samples and 24 attributes taken from UCI for classification of chronic kidney disease with particle swarm optimization (PSO) based feature selection method. As a result of the study, the results of the application of each data mining method are compared with the resultant training and test results. As a result of the comparison, the best performance was found to be 99.75% accuracy with PSO and random subspace method. Moreover, as a method of data mining, it has been seen that the random subspace method has higher accuracy rates than the other methods.