Chronic Kidney Disease Prediction with Reduced Individual Classifiers

DOI: 10.26650/electrica.2018.99255Chronic kidney disease is a rising healthproblem and involves conditions that decrease the efficiency of renal functionsand that damage the kidneys. Chronic kidney disease may be detected withseveral classification techniques, and these have been classified using variousfeatures and classifier combinations. In this study, we applied seven differentclassifiers (Naïve Bayes, HoeffdingTree, RandomTree, REPTree, Random Subspaces,Adaboost, and IBk) for the diagnosis of chronic kidney disease. Theclassification performances are evaluated with five different performancemetrics, i.e., accuracy, kappa, mean absolute error (MAE), root mean squareerror (RMSE), and F measures. Considering the classification performanceanalyses of these methods, six reduced features provide a better and more rapidclassification performance. Seven individual classifiers are applied to the sixfeatures and the best results are obtained using individual random tree and IBkclassifiers.
DOI: 10.26650/electrica.2018.99255Chronic kidney disease is a rising health problem and involves conditions that decrease the efficiency of renal functions and that damage the kidneys. Chronic kidney disease may be detected with several classification techniques, and these have been classified using various features and classifier combinations. In this study, we applied seven different classifiers (Naïve Bayes, HoeffdingTree, RandomTree, REPTree, Random Subspaces, Adaboost, and IBk) for the diagnosis of chronic kidney disease. The classification performances are evaluated with five different performance metrics, i.e., accuracy, kappa, mean absolute error (MAE), root mean square error (RMSE), and F measures. Considering the classification performance analyses of these methods, six reduced features provide a better and more rapid classification performance. Seven individual classifiers are applied to the six features and the best results are obtained using individual random tree and IBk classifiers.