Prediction of parastomal hernia in patients undergoing preventive ostomy after rectal cancer resection using machine learning

Table 2 Performance metrics for five different models in the training cohort

Model	AUC (95%CI)	Accuracy	Sensitivity	Specificity	PPV	NPV
RF	0.909 (0.844-0.974)	0.891	0.857	0.893	0.305	0.991
LR	0.876 (0.800-0.953)	0.785	0.809	0.783	0.170	0.986
KNN	0.884 (0.825-0.954)	0.920	0.714	0.932	0.365	0.983
XGBoost	0.879 (0.812-0.945)	0.829	0.761	0.833	0.200	0.984
SVM	0.870 (0.786-0.955)	0.859	0.857	0.859	0.250	0.990

SVM: Support vector machine; LR: Logistic regression; KNN: K-nearest neighbors; RF: Random forest; XGBoost: EXtreme gradient boosting; NPV: Negative predictive value; PPV: Positive predictive value; AUC: Area under the curve.