Development and validation of an interpretable machine learning model for predicting acute kidney injury after pancreatic surgery

Table 3 Machine learning evaluation metrics for the training and validation cohorts

Models	AUC (95%CI)	Accuracy	Sensitivity	Specificity	Brier score
Developing set
CatBoost	0.803 (0.759, 0.848)	0.738	0.780	0.710	0.180
KNN	0.772 (0.725, 0.819)	0.703	0.791	0.609	0.195
LightGBM	0.786 (0.740, 0.832)	0.722	0.764	0.676	0.192
LR	0.776 (0.728, 0.824)	0.724	0.754	0.693	0.189
RF	0.782 (0.735, 0.828)	0.719	0.780	0.654	0.191
SVM	0.765 (0.716, 0.813)	0.700	0.733	0.665	0.197
XGBoost	0.781 (0.734, 0.827)	0.730	0.780	0.676	0.191
Validation set
CatBoost	0.751 (0.652, 0.850)	0.702	0.767	0.647	0.203
KNN	0.655 (0.544, 0.766)	0.596	0.674	0.529	0.232
LightGBM	0.756 (0.657, 0.854)	0.681	0.651	0.706	0.203
LR	0.709 (0.605, 0.812)	0.617	0.651	0.589	0.218
RF	0.712 (0.607, 0.817)	0.628	0.651	0.608	0.216
SVM	0.714 (0.611, 0.817)	0.638	0.674	0.608	0.212
XGBoost	0.730 (0.627, 0.833)	0.681	0.674	0.686	0.210

AUC: Area under the curve; CI: Confidence interval; CatBoost: Categorical boosting; KNN: K-nearest neighbor; LightGBM: Light gradient boosting machine; LR: Logistic regression; RF: Random forest; SVM: Support vector machine; XGBoost: Extreme gradient boosting with classification trees.