A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering.

ABSTRACT: In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Ga

SUBMITTER: Ikponmwoba E 

PROVIDER: S-EPMC9405612 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature