Table 3.

Prediction Efficiency of Each Machine Learning Models

Models	AUC	F1-Score	Accuracy	Precision	Sensitivity	Specificity
XGBoost	0.777	0.788	0.716	0.839	0.755	0.617
DT	0.738	0.822	0.745	0.820	0.834	0.516
RF	0.802	0.857	0.784	0.819	0.904	0.480
kNN	0.741	0.823	0.733	0.783	0.874	0.373
SVM	0.787	0.754	0.691	0.868	0.681	0.714
FCN	0.791	0.849	0.774	0.817	0.892	0.474

Abbreviations: AUC, area under curve; DT, decision tree; FCN, fully convolutional networks; kNN, k-nearest neighbor; RF, random forest; SVM, support vector machine.