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. 2026 Sep 16;16:1891664. doi: 10.3389/fcimb.2026.1891664

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Copyright © 2026 Yang, Huang, Luo and Peng.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Panel A displays a Lasso regression coefficients trace plot for variable selection, with each colored line representing a different variable. Panels B, C, and D are horizontal bar charts showing variable permutation importance for Random Forest, Gradient Boosting, and Support Vector Machine models respectively, using blue bars for positive and red for negative importances. Panel E is a Venn diagram illustrating overlap and unique variable selection among the four models, highlighting seven variables common to all. — Machine learning selection of core predictors for 28-day ICU mortality. Variable importance from (A) random forest, (B) gradient boosting machine, (C) Lasso regression coefficients, and (D) support vector machine. (E) Venn diagram showing the intersection of seven core predictors (logLDAR, RDW, CO₂CP, LAC, INR, TB, UREA) identified by all four algorithms.