Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease

Table 1

Comparison of different classifiers applied on the DKD dataset.

Classifier	Execution time (seconds)	Accuracy (%)	Correctly classified instances	Incorrectly classified instances
IBK	0	93.6585	384	26
Random tree	0.01	93.6585	384	26
Random forest	0.28	93.4146	383	27
Multilayer perceptron	8.3	93.1707	382	28
J48	0.13	89.7561	368	42
Hoeffding tree	0.04	86.0976	353	57
REP tree	0.08	85.122	349	61
Naïve bayes	0.01	80.9756	332	78
AdaBoostM1	0.11	79.0244	324	86