Serum calcium-based interpretable machine learning model for predicting anastomotic leakage after rectal cancer resection: A multi-center study

Table 2 Evaluation indicators in testing set of 10 machine learning models

Model	Accuracy	Sensitivity/PPV	Specificity/NPV	F1
Logistic.R	0.916	0.98	0.912	0.521
SVM.R	0.873	0.76	0.879	0.355
GBM.R	0.901	0.96	0.898	0.471
NeuralNetwork.R	0.89	0.98	0.885	0.455
RandomForest.R	0.974	0.56	0.994	0.667
XGBoost.R	0.925	0.92	0.927	0.708
KNN.R	0.919	0.88	0.921	0.5
Adaboost.R	0.903	0.8	0.908	0.43
LightGBM.R	0.916	0.92	0.915	0.5
CatBoost.R	0.906	0.96	0.904	0.485
Logistic.S	0.914	0.98	0.91	0.515
SVM.S	0.952	0.92	0.954	0.639
GBM.S	0.956	0.96	0.956	0.667
NeuralNetwork.S	0.958	0.96	0.958	0.676
RandomForest.S	0.95	0.76	0.96	0.585
XGBoost.S	0.939	0.96	0.938	0.593
KNN.S	0.952	0.84	0.958	0.618
Adaboost.S	0.804	0.92	0.798	0.301
LightGBM.S	0.927	0.96	0.925	0.545
CatBoost.S	0.943	0.96	0.942	0.608

Models denoted with 'S' are established using data post-SMOTE resampling, while those denoted with 'R' are constructed from the original data set. PPV: Positive predictive value; NPV: Negative predictive value; SVM: Support vector machine; GBM: Gradient boosting machines; XGBoost: eXtreme Gradient Boosting; KNN: K-nearest neighbors; LightGBM: Light gradient boosting machine.