Performance Comparison of Some Classifiers on Chronic Kidney Disease Data

Abstract

In this study, dataset named Chronic_Kidney_Disease obtained from UCI database is used. The dataset consists of 400 individuals' information and contains 25 features. With WEKA software, this dataset was classified according to whether it is chronic kidney disease using Naive Bayes (NB), K-Star, Support Vector Machines (SVM) and J48 classifiers used in data mining. Accuracy, precision, sensitivity, and F-measure values are used for performance comparisons of the performed classifications. According to the obtained results, more successful results were obtained in J48 algorithm with 99% accuracy.