Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study

Table 4 Performance of different models for predicting postpancreatectomy acute pancreatitis

Model	Training AUC	Testing AUC	Specificity	Sensitivity	MCC	Kappa	NPV	PPV
LR	0.605	0.69	0.524	0.857	0.295	0.214	0.943	0.286
RF	0.824	0.815	0.508	1	0.398	0.273	1	0.311
GBDT	0.875	0.735	0.81	0.643	0.392	0.379	0.911	0.429
XGBoost	0.871	0.706	0.762	0.714	0.392	0.365	0.923	0.4
LGBM	0.87	0.73	0.746	0.714	0.375	0.345	0.922	0.385
CatBoost	0.859	0.822	0.667	0.857	0.408	0.343	0.955	0.364

AUC: Area under the receiver operating characteristic curve; MCC: Matthews correlation coefficient; NPV: Negative predictive value; PPV: Positive predictive value; LR: Logistic regression; RF: Random forest; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting; LGBM: Light gradient boosting machine; CatBoost: Category boosting.