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

Table 1 Baseline characteristics of patients with and without anastomotic leakage, n (%)

	NAL (n = 1732)	AL (n = 86)	P value
Sex = male	1055 (60.9)	64 (74.4)	0.016
Tobacco or alcohol			< 0.001
No	1256 (72.5)	46 (53.5)
Tobacco	258 (14.9)	18 (20.9)
Alcohol	25 (1.4)	1 (1.2)
All	193 (11.1)	21 (24.4)
T stage			< 0.001
T1	192 (11.1)	2 (2.3)
T2	453 (26.2)	17 (19.8)
T3	995 (57.4)	43 (50.0)
T4	92 (5.3)	24 (27.9)
N stage			0.025
N0	291 (16.8)	12 (14.0)
N1	1260 (72.7)	57 (66.3)
N2	181 (10.5)	17 (19.8)
Histological type			< 0.001
Adenocarcinoma	36 (2.1)	0 (0.0)
Adenosquamouscarcinoma	0 (0.0)	6 (7.0)
Others	1663 (96.0)	80 (93.0)
Multi	1 (0.1)	0 (0.0)
Neuroendocrine	17 (1.0)	0 (0.0)
Stromal	15 (0.9)	0 (0.0)
Family = yes	32 (1.8)	10 (11.6)	< 0.001
CD34 = yes	647 (37.4)	67 (77.9)	< 0.001
FOBT = M1	1276 (73.7)	64 (74.4)	0.978
Location = others	76 (4.4)	2 (2.3)	0.517
Age = high	871 (50.3)	39 (45.3)	0.433
Neoadjuvant = yes	142 (8.2)	57 (66.3)	< 0.001

NAL: Non-anastomotic leakage; AL: Anastomotic leakage.

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.