Fig. 2

Candidate predictors selection by LASSO. A. Coefficient trendlines of 19 variables for GDM diagnosis. The coefficient profile plot was created against the log (λ) sequence. By obtaining a set of regression coefficients (β), the method aimed to minimize the sum of the RSS and the penalty term (λ). By adjusting λ, certain β values corresponding to specific variables were reduced to zero, thereby achieving effective variable selection. B Tuning parameter (λ) selection of deviance in the LASSO regression based on the minimum criteria (left dotted line) and the 1-SE criteria (right dotted line), where 12 nonzero coefficients were selected, including age, prepregnancy BMI, PCOS, history of GDM, family history of diabetes, WBC, RBC, HCT, lg (ALT), UA, FPG, and urine glucose. Notes: LASSO, least absolute shrinkage and selection operator; λ, lambda; RSS, residual sum of squares; SE, standard error.