Severity prediction markers in dengue: a prospective cohort study using machine learning approach.

Abstract

Background: Dengue virus causes illnesses with or without warning indicators for severe complications. There are no clear prognostic signs linked to the disease outcomes.

Methods: Clinical and laboratory parameters among 102 adult including 17 severe dengue (SD), 33 with warning and 52 without warning signs during early and critical phases were analysed by statistical and machine learning (ML) models.

Results: In classical statistics, abnormal ultrasound findings, platelet count and low lymphocytes were significantly linked with SD during the febrile phase, while low creatinine, high sodium and elevated AST/ALT during the critical phase. ML models highlighted AST/ALT and lymphocytes as key markers for distinguishing SD from non-severe dengue, aiding clinical decisions.

Conclusion: Parameters like liver enzymes, platelet counts and USG findings were linked with SD.USG testing at an earlier phase of dengue and a point-of-care system for the quantification of AST/ALT levels may lead to an early prediction of SD.