Table 2.

Comparative Analysis of Performance Results for Different Machine Learning Models

Models	AUC	Accuracy	Sensitivity	Specificity	PPV	NPV	F1 Score	Brier Score
RF	0.83	0.72	0.74	0.70	0.72	0.72	0.73	0.176
DT	0.67	0.67	0.67	0.67	0.68	0.66	0.68	0.346
XGBoost	0.80	0.73	0.76	0.70	0.72	0.73	0.74	0.211
SVM	0.75	0.68	0.75	0.62	0.67	0.70	0.71	0.205
LR	0.73	0.66	0.68	0.63	0.67	0.66	0.67	0.209
LightGBM	0.80	0.72	0.76	0.68	0.71	0.73	0.73	0.194
MLP	0.73	0.66	0.71	0.61	0.65	0.67	0.68	0.208

Abbreviations: RF, Random Forest; DT, Decision Tree; XGBoost, extreme gradient Boosting; SVM, Support Vector Machine; LR, Logistic Regression; LightGBM, light gradient boosting machine; MLP, Multilayer Perceptron; AUC, the area under the receiver-operating characteristic; PPV, positive predictive value; NPV, negative predictive value.