Predicting esophageal cancer response to neoadjuvant therapy with magnetic resonance imaging radiomics

Table 3 Delong test for comparing area under the curves among classification algorithms

Models	LR	SVM	KNN	RF	ET	XGBoost	LGBM	AdaBoost	MLP
LR		0.405	0.900	0.480	0.194	0.794	1.000	1.000	0.820
SVM	0.405		0.729	0.194	0.110	0.852	0.648	0.546	0.863
KNN	0.900	0.729		0.637	0.095	0.893	0.895	0.859	0.873
RF	0.480	0.194	0.637		0.230	0.582	0.710	0.626	0.648
ET	0.194	0.110	0.095	0.230		0.230	0.275	0.160	0.246
XGBoost	0.794	0.852	0.893	0.582	0.230		0.480	0.745	1.000
LGBM	1.000	0.648	0.895	0.710	0.275	0.480		1.000	0.777
AdaBoost	1.000	0.546	0.859	0.626	0.160	0.745	1.000		0.794
MLP	0.820	0.820	0.873	0.648	0.246	1.000	0.777	0.794

LR: Logistic regression; SVM: Support vector machines; KNN: K-nearest neighbor; RF: Random forest; ET: ExtraTrees; XGBoost: Extreme gradient boosting; LGBM: Light gradient boosting machine; AdaBoost: Adaptive boosting; MLP: Multilayer perceptron.