Table 9.

Mortality model performance based on area under the receiver operating characteristic curve (AUROC).

Method and algorithm		Training set AUROC, mean (SD)	Test set AUROC
Baseline approach
	LRa	0.702047 (0.015652)	0.69313
	LDAb	0.701731 (0.016077)	0.69247
	RFc	0.764875 (0.009214)	0.76725
	kNNd	0.629262 (0.008944)	0.63173
	SVMe	0.653269 (0.011730)	0.66800
	XGBf	0.771187 (0.012094)	0.76971
Quantiles approach
	LR	0.727331 (0.014217)	0.72810
	LDA	0.725909 (0.014758)	0.72622
	RF	0.783696 (0.010503)	0.78292
	KNN	0.631649 (0.010416)	0.64087
	SVM	0.719253 (0.008940)	0.72333
	XGB	0.788908 (0.010665)	0.79036

^aLR: logistic regression.

^bLDA: linear discriminant analysis.

^cRF: random forest.

^dkNN: k-nearest neighbor.

^eSVM: support vector machine.

^fXGB: extreme gradient boosting.