Comparison of Different Machine Learning Techniques to Predict Diabetic Kidney Disease

Table 2

Classification results from WEKA.

Classifier	Kappa statistics (K)	Mean absolute error (MAE)	Root mean squared error (RMSE)
IBK	0.8731	0.1096	0.2496
Random tree	0.8731	0.1093	0.2497
Random forest	0.8681	0.1267	0.2542
Multilayer perceptron	0.8633	0.1117	0.2513
J48	0.7947	0.1595	0.3074
Hoeffding tree	0.7223	0.1389	0.3696
REP tree	0.7025	0.2194	0.3565
Naïve bayes	0.6199	0.1899	0.4261
AdaBoostM1	0.5827	0.3246	0.4009