Table 3.

Comparison of multiple evaluation metrics with different machine learning methods in the binary classification scenario

Branch	Acc (%)	Pre (%)	Sp (%)	Se (%)	F1-score (%)	ROC AUC	PR AUC	MCC
Catboost	87.30	83.82	82.81	91.94	87.69	0.8805	0.8764	0.7498
SVM	79.37	80.00	81.25	77.42	78.69	0.8124	0.8043	0.5873
Decision Tree	84.12	81.82	81.25	87.10	84.38	0.8793	0.8702	0.6842
Random Forest	82.54	80.30	79.69	85.48	82.81	0.8627	0.8544	0.6524
KNN	77.78	78.33	79.69	75.81	77.05	0.8032	0.7842	0.5555
MLP	67.46	66.67	67.19	67.74	67.20	0.6844	0.6709	0.3493
Ensemble learning	95.24	96.67	96.87	93.55	95.08	0.9591	0.9538	0.9052