Table A1.

Optimal Hyperparameters of All Machine Learning Models.

Model	Optimal Hyperparameters
LR	nIter = 21
KNN	k = 7
NB	usekernal, Laplace = 0, Adjust = 1
DT	Maximum depth = 5 Criterion = Gini index
RF	Mtry * = 3
GBM	Maximum depth = 3 Number of estimators = 50, Gamma = 0
SVM	degree = 3, scale = 0.1 and C = 1.0
ANN	Number of hidden layers = 2 Number of nodes in a layer = 20, 10

LR, logistic regression; KNN, k-nearest neighbors; NB, naïve Bayes; DT, decision tree; RF, random forest; GBM, gradient boosting machine; SVM, support vector machine; ANN, artificial neural networks. * mtry indicates the number of variables available for splitting at each tree node.