Chronic Kidney Disease Prediction with Reduced Individual Classifiers

Year 2018, Volume: 18 Issue: 2, 249 - 255, 03.08.2018

Merve Doğruyol Başar , Aydın Akan

Abstract

DOI: 10.26650/electrica.2018.99255

Chronic 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.

Keywords

Chronic kidney disease, reduced individual classifiers, data mining, classification

Chronic Kidney Disease Prediction with Reduced Individual Classifiers

Abstract

DOI: 10.26650/electrica.2018.99255

Chronic 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.

Keywords

reduced individual classifiers, data mining, classification, Chronic kidney disease, classification

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