Figure 2.

Optimal feature selection via a tri-algorithm intersection strategy. (A) Box plot of variable importance using the Boruta algorithm. (B) Boruta feature filtering importance score plot across classifier runs. (C) The LASSO coefficient profiles of the clinical features against the λ sequence. (D) Selection of the optimal penalization coefficient (λ) in the LASSO model via 10-fold cross-validation. (E) RFE indicating the optimal number of variables based on cross-validation accuracy. (F) A Venn diagram illustrating the intersection of the robust predictors uniformly identified by Boruta, LASSO, and RFE, resulting in a core subset of 10 features. LASSO, least absolute shrinkage and selection operator; RFE, recursive feature elimination.