Table 2.

Model performance metrics

Models	AUC	Recall	Accuracy	F1 score	Sensitivity	Specificity
LR	0.737	0.796	0.765	0.858	0.834	0.878
KNN	0.664	0.798	0.742	0.840	0.886	0.857
SVM	0.735	0.797	0.788	0.874	0.833	0.926
Decision tree	0.749	0.834	0.793	0.870	0.910	0.882
Random forest	0.779	0.809	0.794	0.876	0.935	0.923
XGBoost	0.817	0.852	0.832	0.895	0.943	0.913
ANN	0.755	0.778	0.783	0.875	0.824	0.899
SOFA	0.646	0.755	0.723	0.781	0.633	0.712
SAPS II	0.702	0.774	0.762	0.814	0.811	0.845

AUC area under curve, LR logistic regression KNN: k-nearest neighbors, SVM support vector machine, XGBoost extreme gradient boosting, ANN artificial neural network, SOFA Sequential Organ Failure Assessment, SAPS II the Simplified Acute Physiology Score II