Machine learning for modeling and identifying risk factors of pancreatic fistula

Table 3 Quality of binary models, mean ± SD

Algorithm model	Logistic regression	Random forest	CatBoost
5-fold cross validation ROC AUC1
Pre- and intraoperative data model	0.71 ± 0.11	0.77 ± 0.08	0.74 ± 0.1
3-5 days after surgery	0.86 ± 0.09	0.8 ± 0.11	0.87 ± 0.07
5-fold cross validation ROC AUC2
Pre- and intraoperative data model	0.72	0.74	0.74
3-5 days after surgery	0.81	0.81	0.86

¹mean ± SD receiver operating characteristic area under the curve of each fold of cross-validation, n = 20% of the initial dataset.

²Integral receiver operating characteristic area under the curve calculated with all false positive rates obtained in 5-fold cross-validation. True positive rate was obtained from the initial dataset.

ROC: Receiver operating characteristic; AUC: Area under the curve.