Table 2.

Model performance

Data set	Model	AUC	AUC 95% CI Lower	AUC 95% CI Upper	Accuracy	Precision	Sensitivity	Specificity	F1 Score
Train set	Logistic	0.759	0.725	0.794	0.716	0.491	0.494	0.802	0.492
	Decision Tree	0.860	0.832	0.888	0.802	0.641	0.657	0.858	0.649
	Random Forest	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
	XGBoost	0.987	0.978	0.996	0.951	0.905	0.922	0.963	0.913
	LightGBM	0.970	0.956	0.984	0.926	0.855	0.886	0.942	0.870
	SVM	0.753	0.718	0.787	0.757	0.600	0.380	0.902	0.465
	ANN	0.762	0.728	0.796	0.708	0.480	0.572	0.760	0.522
Test set	Logistic	0.721	0.666	0.776	0.742	0.548	0.472	0.848	0.507
	Decision Tree	0.639	0.580	0.698	0.668	0.408	0.403	0.772	0.406
	Random Forest	0.753	0.700	0.806	0.730	0.518	0.611	0.777	0.561
	XGBoost	0.763	0.711	0.815	0.762	0.580	0.556	0.842	0.567
	LightGBM	0.784	0.734	0.835	0.793	0.638	0.611	0.864	0.624
	SVM	0.706	0.651	0.762	0.734	0.559	0.264	0.918	0.358
	ANN	0.716	0.661	0.771	0.703	0.475	0.528	0.772	0.500

AUC: area under the curve;

XGBoost: eXtreme Gradient Boosting; LightGBM: Light Gradient Boosting Machine; SVM: Support Vector Machine; ANN: Artificial Neural Network;