Table 4.

Hyperparameters selected to be fed into the classifiers for the early prediction of gastric cancer

Num	ML models	Hyper-parameters	F-score
1	RF	(‘verbose’:2,’random_state’:888,’n_estimators’:10,’max_deph’:9,’criterion’: gini’)	85.31
2	MLP	‘Learning rate’ = ’constant’, hidden_layer_size’ = (80, 80, 80), ‘alpha’ = 0.08, ‘activation’ = ’rulo’	87.6
3	SVM (kernel = linear)	C = 100, G = 0.0001	83.04
4	SVM (kernel = RBF)	C = 10, G = 0.001	81.9
5	XGBoost	‘min_chid_weigh’ = 1’max_depht’ = 14,’learning_rate’ = 0.2, ‘gamma’ = 0.4, ‘colsample_bytree’ = 0.5	81.02