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Article

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

1
Department of Mechanical and Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA
2
Department of Pediatrics, Center for Blood Oxygen Transport and Hemostasis, University of Maryland Baltimore School of Medicine, Baltimore, MD 21201, USA
3
Department of Nuclear Engineering, The Pennsylvania State University, University Park, PA 16802, USA
*
Authors to whom correspondence should be addressed.
Biosensors 2022, 12(8), 589; https://doi.org/10.3390/bios12080589
Received: 29 June 2022 / Revised: 27 July 2022 / Accepted: 28 July 2022 / Published: 2 August 2022
(This article belongs to the Special Issue High-Efficiency Surface-Enhanced Raman Scattering Biosensing)
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 Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high. View Full-Text
Keywords: surface-enhanced Raman spectroscopy; machine learning; COVID-19; Gaussian processes surface-enhanced Raman spectroscopy; machine learning; COVID-19; Gaussian processes
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MDPI and ACS Style

Ikponmwoba, E.; Ukorigho, O.; Moitra, P.; Pan, D.; Gartia, M.R.; Owoyele, O. A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering. Biosensors 2022, 12, 589. https://doi.org/10.3390/bios12080589

AMA Style

Ikponmwoba E, Ukorigho O, Moitra P, Pan D, Gartia MR, Owoyele O. A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering. Biosensors. 2022; 12(8):589. https://doi.org/10.3390/bios12080589

Chicago/Turabian Style

Ikponmwoba, Eloghosa, Okezzi Ukorigho, Parikshit Moitra, Dipanjan Pan, Manas Ranjan Gartia, and Opeoluwa Owoyele. 2022. "A Machine Learning Framework for Detecting COVID-19 Infection Using Surface-Enhanced Raman Scattering" Biosensors 12, no. 8: 589. https://doi.org/10.3390/bios12080589

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