Development and validation of a postoperative prediction model for residual stones after hepatectomy in patients with hepatolithiasis

Table 3 Comparative performance of the 12 candidate models based on development-cohort out-of-fold predictions

Candidate algorithm	AUC	Brier score	Log loss	Calibration intercept	Calibration slope
LASSO-LR	0.849	0.138	0.432	0.022	1.748
XGBoost	0.817	0.153	0.469	0.227	0.705
GBM	0.813	0.150	0.457	0.076	0.938
RF	0.803	0.154	0.472	0.016	1.085
CatBoost	0.801	0.167	0.518	0.510	0.589
LDA	0.796	0.171	0.565	0.122	0.434
NB	0.788	0.187	0.689	0.937	0.334
RBF-SVM	0.785	0.158	0.481	-0.055	0.939
PLSR	0.784	0.175	0.535	-0.581	2.237
LightGBM	0.780	0.183	0.679	0.855	0.338
ANN	0.757	0.172	0.568	0.047	0.475
k-NN	0.743	0.180	0.596	0.723	0.709

Values were derived from patient-level out-of-fold predictions generated via 15 outer resampled iterations within the development cohort (n = 211). Bold formatting highlights the pre-specified primary model. LASSO-LR: Least absolute shrinkage and selection operator-logistic regression; XGBoost: EXtreme Gradient Boosting; GBM: Gradient boosting machine; RF: Random forest; CatBoost: Categorical boosting; LDA: Linear discriminant analysis; NB: Naive Bayes; RBF-SVM: Support vector machine with radial basis function kernel; PLSR: Partial least squares regression; LightGBM: Light gradient boosting machine; ANN: Artificial neural network; k-NN: K-nearest neighbors; AUC: Area under the receiver operating characteristic curve.