Fig. 3

Lasso regression conducted the feature selection for the relationship between EASIX and in-hospital mortality. A Variation characteristics of the coefficients of variables as the regularization parameter λ changes. The plot shows how the coefficients shrink towards zero with increasing λ, highlighting the importance of each variable; B selection process of the optimal value of the regularization parameter λ in the Lasso regression model, determined through cross-validation. The plot illustrates the relationship between the mean cross-validation error and log (λ). The dashed vertical lines indicate two key values of λ: the value that minimizes the mean cross-validation error (λ_min) and the largest value of λ within one standard error of the minimum (λ_1se), used for model selection