Predictive value of machine learning models for lymph node metastasis in gastric cancer: A two-center study

Table 2 Prediction performance evaluation of each model

Model	AUC	Accuracy	Kappa	Sensitivity (recall rates)	Specificity
DT	0.824	0.821	0.638	0.806	0.843
RF	0.923	0.854	0.702	0.847	0.882
SVM	0.721	0.585	0.000	0.750	0.547
GBM	0.927	0.870	0.734	0.875	0.863
NB	0.914	0.821	0.640	0.861	0.843
MLP	0.907	0.837	0.665	0.882	0.824
LR	0.898	0.821	0.636	0.806	0.882

AUC: Area under the receiver operating characteristic curve; DT: Decision tree; RF: Random forest; SVM: Support vector machine; GBM: Gradient boosting machine; NB: Naive bayes; LR: Logistic regression; MLP: Multi-layer perceptron.