Machine learning-based prediction of postoperative mortality risk after abdominal surgery

Table 4 Model prediction efficiency

Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	Recall	Precision
Nomogram	0.908 (0.824-0.992)	0.899	0.962	0.688	0.962	0.911
Decision-tree	0.874 (0.785-0.963)	0.855	0.849	0.875	0.849	0.957
Random-forest	0.928 (0.869-0.987)	0.841	0.906	0.625	0.906	0.889
Gradient-boosting tree	0.907 (0.837-0.976)	0.870	0.906	0.750	0.906	0.923
Support vector machine	0.983a,2,4 (0.959-1.000)	0.884	0.925	0.750	0.925	0.925
Naive Bayes	0.807a,1,3,4,5 (0.702-0.911)	0.797	0.811	0.750	0.811	0.915

^aP < 0.05.

¹P vs nomogram.

²P vs decision-tree model.

³P vs random-forest model.

⁴P vs gradient-boosting tree model.

⁵P vs support vector machine model.

AUC: Area under the receiver operating characteristic curve; CI: Confidence interval.