Table 3.

Comparison of Classification Performance of Four Models with Oversampling Technique in Terms of Receiver Operating Characteristic Area Under the Curve Based on Different Groups of Autoantibodies (i.e., Ab+ vs. Ab–) in Different Scenarios, When We Defined the Four Models by Using All the Features for Each Scenario

Classification models	AUC-ROC Overall CGM (12 features)	AUC-ROC Overnight (12 features)	AUC-ROC SLMM (9 features)	AUC-ROC Overnight and SLMM (21 features)
LDA	0.679	0.679	0.804	0.693
SVM + Linear Kernel	0.672	0.812	0.825	0.776
LR	0.692	0.765	0.778	0.715
KNN	0.639	0.621	0.776	0.760

AUC-ROC, receiver operating characteristic area under the curve; CGM, continuous glucose monitoring; IAUC, incremental area under the curve; KNN, K-nearest neighbors; LDA, linear discriminant analysis; LR, logistic regression; SLMM, standardized liquid mixed meals; SVM, support vector machine. AUC-ROC values in boldface indicate the best performance.