Machine-learning-based prediction model for Clavien-Dindo grade ≥ II complications after neoadjuvant therapy and laparoscopic gastrectomy in gastric cancer

Table 2 Performance metrics of the models

Model	AUC	Accuracy	Recall	Sensitivity	Specificity
NNE	0.789 (0.739-0.840)	0.732 (0.657-0.807)	0.731 (0.610-0.851)	0.731 (0.610-0.851)	0.733 (0.625-0.841)
Logistic regression	0.786 (0.606-0.966)	0.752 (0.697-0.807)	0.763 (0.525-0.824)	0.763 (0.525-0.824)	0.756 (0.704-0.807)
GLMnet	0.776 (0.668-0.884	0.67 (0.555-0.786)	0.739 (0.570-0.908)	0.739 (0.570-0.908)	0.656 (0.508-0.803)
Random forest	0.754 (0.648-0.861)	0.666 (0.641-0.691)	0.735 (0.505-0.965)	0.735 (0.505-0.965)	0.659 (0.598-0.720)
XGBoost	0.703 (0.571-0.835)	0.679 (0.567-0.791)	0.606 (0.400-0.813)	0.606 (0.400-0.813)	0.693 (0.577-0.810)
Decision tree	0.602 (0.544-0.660)	0.756 (0.670-0.842)	0.371 (0.259-0.482)	0.371 (0.259-0.482)	0.827 (0.725-0.928)

NNE: Neural network ensemble; AUC: Area under the curve.