. 2024 Apr 15;10:20552076241234746. doi: 10.1177/20552076241234746

Table 2.

Predictive performance comparison of the four machine learning models in the test cohort.

Parameters	XGB^a	RF^b	LR^c	MLP^d
Sensitivity	0.89	0.5	0.72	0.67
specificity	0.92	1	0.96	0.95
Positive predictive value (%)	66.43	100	76.20	70.44
Negative predictive value (%)	97.92	91.93	95.07	94.18
Accuracy	0.92	0.92	0.92	0.91
Precision	0.67	1	0.76	0.71
F1 score	0.76	0.67	0.74	0.68
AUC	0.96	0.96	0.95	0.96
Average precision	0.84	0.85	0.77	0.85

^{^a}

XGB: XGBoost.

^{^b}

RF: Random forest.

^{^c}

LR: Logistic regression.

^{^d}

MLP: Multilayer perceptron.