Table IV.

Performance of postoperative prediction models reported as AUC (95% CI)

	LR	SVM	RFDT	XGB machines	Ensemble model (LR, RFDT, XGB)
Fold 1	0.7152 (0.6844–0.7460)	0.5996 (0.5677–0.6313)	0.7122 (0.6813–0.7430)	0.7165 (0.6857–0.7473)	0.7219 (0.6912–0.7526)
Fold 2	0.7142 (0.6821–0.7463)	0.4824 (0.4510–0.5139)	0.7030 (0.6707–0.7353)	0.7112 (0.6790–0.7434)	0.7169 (0.6848–0.7489)
Fold 3	0.7133 (0.6822–0.7443)	0.5200 (0.4887–0.5511)	0.7152 (0.6842–0.7463)	0.7218 (0.6909–0.7527)	0.7266 (0.6957–0.7574)
Fold 4	0.7074 (0.6767–0.7382)	0.4791 (0.4492–0.5090)	0.7198 (0.6893–0.7503)	0.7238 (0.6933–0.7542)	0.7291 (0.6987–0.7594)
Fold 5	0.7269 (0.6962–0.7575)	0.5122 (0.4813–0.5432)	0.7350 (0.7045–0.7655)	0.7295 (0.6990–0.7602)	0.7388 (0.7084–0.7692)
Average 5-fold CV accuracy	0.7150 (0.7010–0.7289)	0.5136 (0.4997–0.5275)	0.7170 (0.7031–0.7309)	0.7205 (0.7067–0.7344)	0.7266 (0.7128–0.7404)
20% test data*,†	0.6955 (0.6756–0.7304) Sensitivity: 0.6261 Specificity: 0.6509	0.5142 (0.5020–0.5264) Sensitivity: ‡NA Specificity: ‡NA	0.6953 (0.6756–0.7304) Sensitivity: 0.6712 Specificity: 0.6067	0.7030 (0.6756–0.7304) Sensitivity: 0.6441 Specificity: 0.6667	0.7074 (0.6871–0.7277) Sensitivity: 0.6464 Specificity: 0.6454

^*Results for 20% test data reported as AUC with 95% CI, sensitivity and specificity.

^†Sensitivity and specificity reported using a cutoff value of 0.015 on predicted probabilities (0.015 was chosen as this provided the most balanced Sensitivity and Specificity on the ensemble model).

^‡Not calculated due to poor cross-validation performance.