The Impact on the Classification Performance of the Combined Use of Different Classification Methods and Different Ensemble Algorithms in Chronic Kidney Disease Detection

Abstract

The aim of this study is to compare the performance assessment results of the different classification methods and ensemble algorithms for the detection of chronic kidney disease. Six different basic classifier (naive bayes, k nearest neighbor (KNN), support vector machines (SVM), J48, random trees, decision tables) and three different ensemble algorithm (adaboost, bagging, random subspace) are used in the study. Classification results were evaluated using three different performance evaluation criteria ( accuracy, kappa, the area under the ROC curve (AUC)). According to the performance evaluation results, J48 basis algorithm for use with bagging and random subspace ensemble algorithms and random tree basis algorithm for use with bagging ensemble algorithm has provided 100% classification success.