Machine learning for modeling and identifying risk factors of pancreatic fistula

Table 4 Quality of three-dimensional models, mean ± SD

Algorithm model	Logistic regression	Random forest	CatBoost
5-fold cross validation ROC AUC1
Pre- and intraoperative data model	0.71 ± 0.088	0.67 ± 0.09	0.68 ± 0.07
3-5 days after surgery	0.77 ± 0.145	0.726 ± 0.145	0.74 ± 0.16
5-fold cross validation ROC AUC2
Pre- and intraoperative data model: Fistula B/C vs other classes	0.77	0.73	0.85
3-5 days after surgery: Fistula B/C vs other classes	0.76	0.77	0.86
Pre- and intraoperative data model: Fistula B/C vs biochemical leak	0.73	0.45	0.74
3-5 days after surgery: Fistula B/C vs biochemical leak	0.39	0.44	0.47

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. These metrics were calculated for the model’s ability to distinguish different classes.

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