Discrimination of Dengue Diseases in Children Using Surface-Enhanced Raman Spectroscopy Coupled with Machine Learning Approaches.

ABSTRACT: This study introduces a novel approach to dengue diagnostics by leveraging surface-enhanced Raman spectroscopy (SERS) coupled to machine learning. This method addresses the critical need for rapid and accurate identification of dengue virus (DENV) infection and prediction of the disease severity. For the first time, a commercialized SERS substrate is applied to analyze plasma samples from 60 pediatric patients, equally distributed among other febrile illnesses (OFI), dengue fever (DF), and dengue hemorrhagic fever (DHF) cases. This innovative application of SERS technology captures unique molecular signature characteristics of each disease state, offering a new paradigm in viral diagnostics. Our methodology employs various machine learning algorithms, notably linear discriminant analysis (

SUBMITTER: Waiwijit U 

PROVIDER: S-EPMC12291032 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature