Table 4.

List and values of hyperparameters used for machine learning models.

Model	Hyperparameter	Hyperparameter Range
LR	Solver = liblinear, Penalty: l2, C: 3.0	Solver = liblinear, sag, saga, Penalty: l1, l2, C: 1 to 5
RF	n_estimators = 100, max_depth = 12, min_samples_leaf = 0.02	n_estimators = 10 to 200, max_depth = 2 to 50, min_samples_leaf = 0.01 to 0.05
DT	max_depth = 12, min_samples_leaf = 0.02	max_depth = 2 to 50, min_samples_leaf = 0.01 to 0.05
SVM	Kernel = Polynomial, C = 3.0, degree = 1	Kernel = Polynomial, linear, C: 1 to 5, degree = 1 to 5
KNN	n_neighbors = 2	n_neighbors = 1 to 5
ANN	Hidden layers = 2, optimizer = rmsprop, loss = binary_crossentropy, batch_size = 5, epochs = 100	Hidden layers = 2 to 5, optimizer = rmsprop, adam, SGD, loss = binary_crossentropy, batch_size = 5, 10, 15, epochs = 50, 100, 150