Abstract

Dengue pathogenesis involves immune‐driven inflammation that contributes to severe disease progression. This study assessed a machine learning model to identify a minimal, yet highly predictive biomarker set, aiming to support clinical decision‐making and patient triage. A total of 48 inflammatory mediators were quantified from plasma samples collected at admission from confirmed dengue patients, classified as either dengue without warning signs (DF) or dengue with warning signs/severe dengue (DWS/SD). A random forest approach was applied to identify the most predictive biomarkers associated with disease severity requiring hospitalization, based on admission‐time variables. Among the 48 immune mediators, 43 were differentially expressed in dengue patients versus healthy controls, and 26 showed significant differences between DF and DWS/SD cases. Lymphocyte counts negatively correlated with IL‐1RA, while liver enzymes showed positive correlations with HGF and SCGF‐beta; platelet counts also negatively correlated with these markers. Key severity‐associated markers included HGF, TNF‐beta, MIP‐1‐beta, and SCGF‐beta. A model incorporating these markers and fever duration achieved nearly 80% accuracy in distinguishing DWS/SD from DF cases, independent of clinical examination. The findings suggest that targeted cytokine profiling may guide early hospitalization decisions and ease healthcare burdens in dengue‐endemic regions.