Development of a machine learning-based model for predicting postoperative survival in gastric cancer

Table 3 DeLong test between models

Reference	Comparator	AUC reference	AUC comparator	Delta AUC	Z score	P value
ET	KNN	0.853	0.658	0.195	3.299	< 0.050
ET	SVM	0.853	0.660	0.193	3.285	< 0.050
ET	MLP	0.853	0.760	0.091	2.150	< 0.050
ET	GB	0.853	0.790	0.062	1.884	0.060
ET	XGBoost	0.853	0.808	0.044	1.650	0.098
ET	LR	0.853	0.810	0.043	1.473	0.141
ET	LightGBM	0.853	0.820	0.033	1.370	0.170
ET	RF	0.853	0.817	0.036	1.293	0.196
ET	DT	0.853	0.810	0.042	1.168	0.243

ET: Extremely randomized trees; KNN: K-nearest neighbors; SVM: Support vector machine; MLP: Multi-layer perceptron; GB: Gradient boosting; XGBoost: Extreme gradient boosting; LR: Logistic regression; LightGBM: Light gradient boosting machine; RF: Random forest; DT: Decision tree; AUC: Area under the curve.