Table 2.

Description of the traditional feature selection methods and the mean AUROC score in each setting

Traditional feature selection methods	Setting 7	Setting 8	Setting 9
Feature selection models	Univariate feature selection (f_classif)	Lasso (Logistic regression using L1 regularization)	Lasso (Logistic regression using L1 regularization)
Selection methods	SelectKBest (top 3)	SelectFromModel (top 3)	SelectFromModel (top 3)
T2DM classification model	Random forest	Lasso	Random forest
Selected miRNAs	hsa-miR-6820–5p, hsa-miR-29b-2-5p, and hsa-miR-1307-3p	hsa-miR-22-3p, hsa-miR-92a-3p, and hsa-miR-181a-5p	hsa-miR-22-3p, hsa-miR-92a-3p, and hsa-miR-181a-5p
Fold for cross-validation of test data	3	3	3
Mean AUROC score by threefold cross-validation in test set and standard deviation	0.72 ± 0.08	0.64 ± 0.05	0.52 ± 0.02