Table 2.

Comparison of Performance Measures in Machine Learning Models and Stepwise LR Model

Models	R 2	RMSE	Accuracy	AUC
ANN	0.230 ± 0.05	10.71 ± 1.01	0.8130 ± 0.0119	0.7463 ± 0.0191
SVR	0.232 ± 0.03	10.70 ± 0.96	0.7565 ± 0.0603	0.6395 ± 0.0649
KNN	0.104 ± 0.05	11.55 ± 1.07	0.7174 ± 0.1108	0.6393 ± 0.1193
RF	0.100 ± 0.08	11.55 ± 0.90	0.6826 ± 0.0681	0.6272 ± 0.0668
XGBoost	0.178 ± 0.04	11.09 ± 1.21	0.7348 ± 0.0603	0.5921 ± 0.0584
LR	0.094 ± 0.06	11.61 ± 0.76	0.7174 ± 0.0154	0.6881 ± 0.0293

Abbreviations: ANN, artificial neural network; AUC, area under the curve; KNN, K‐nearest neighbor; LR, linear regression; RF, random forest; RMSE, root mean square error; SVR, support vector regression; XGBoost, extreme gradient boosting.