Table 3.

Results of discriminative models in distinguishing pancreatic cystadenomas and pancreatic neuroendocrine tumors in the testing group.

	DC					RF					LASSO					Xgboost					GBDT
	AUC	Accuracy	Sensitivity	Specificity	F1-score	AUC	Accuracy	Sensitivity	Specificity	F1-score	AUC	Accuracy	Sensitivity	Specificity	F1-score	AUC	Accuracy	Sensitivity	Specificity	F1-score	AUC	Accuracy	Sensitivity	Specificity	F1-score
LDA	0.907	0.858	0.797	0.912	0.832	0.915	0.917	0.847	0.971	0.890	0.901	0.867	0.763	0.955	0.832	0.947	0.917	0.817	1.000	0.894	0.918	0.875	0.810	0.929	0.850
SVM	0.853	0.700	0.633	0.757	0.653	0.972	0.725	0.430	0.971	0.542	0.777	0.642	0.267	0.957	0.365	0.946	0.833	0.650	0.986	0.764	0.966	0.908	0.817	0.986	0.886
RF	0.997	0.983	0.980	0.986	0.980	0.994	0.975	0.960	0.986	0.969	0.975	0.950	0.947	0.952	0.948	0.997	0.992	0.980	1.000	0.989	0.989	0.992	0.980	1.000	0.989
Adaboost	0.990	0.967	0.960	0.967	0.961	0.990	0.967	0.960	0.967	0.961	0.976	0.975	1.000	0.952	0.977	0.990	0.975	0.940	1.000	0.964	0.990	0.975	0.943	1.000	0.966
KNN	0.959	0.925	0.870	0.971	0.908	0.983	0.967	0.967	0.971	0.964	0.760	0.675	0.647	0.700	0.634	0.932	0.925	0.890	0.957	0.912	0.969	0.975	0.940	1.000	0.967
GaussianNB	0.973	0.942	0.893	0.986	0.928	0.973	0.775	0.523	0.986	0.654	0.986	0.975	0.963	0.986	0.971	0.926	0.633	0.223	0.971	0.292	0.926	0.608	0.167	0.971	0.209
LR	0.862	0.692	0.613	0.757	0.633	0.946	0.708	0.393	0.971	0.492	0.743	0.675	0.360	0.940	0.458	0.935	0.708	0.377	0.986	0.502	0.909	0.625	0.170	1.000	0.276
GBDT	0.989	0.975	0.980	0.969	0.972	0.979	0.983	0.980	0.986	0.980	0.976	0.975	1.000	0.952	0.977	0.993	0.983	0.980	0.986	0.980	0.983	0.983	0.980	0.983	0.981
DT	0.975	0.975	0.980	0.969	0.972	0.983	0.983	0.980	0.986	0.980	0.976	0.975	1.000	0.952	0.977	0.990	0.992	0.980	1.000	0.989	0.982	0.983	0.980	0.983	0.981

DC, distance correlation; RF, random forest; LASSO, least absolute shrinkage and selection operator; Xgboost, eXtreme gradient boosting; GBDT, gradient boosting decision tree; LDA, linear discriminant analysis; SVM, support vector machine; KNN, k-nearest neighbor; LR, logistic regression; DT, decision tree; AUC, area under curve.