Article
Version 1
Preserved in Portico This version is not peer-reviewed
Machine Learning Data Processing for COVID‐19 Diagnostics
Version 1
: Received: 8 July 2024 / Approved: 9 July 2024 / Online: 10 July 2024 (08:00:11 CEST)
How to cite: Gorbunov, B.; Chapovsky, A. Machine Learning Data Processing for COVID‐19 Diagnostics. Preprints 2024, 2024070727. https://doi.org/10.20944/preprints202407.0727.v1 Gorbunov, B.; Chapovsky, A. Machine Learning Data Processing for COVID‐19 Diagnostics. Preprints 2024, 2024070727. https://doi.org/10.20944/preprints202407.0727.v1
Abstract
A new data processing method based on Machine Learning (ML) algorithms has been developed and tested in clinical environments during COVID-19 trials at the Medical University of South Carolina (MUSC). Breath samples have been collected in 2020–2021 from 100 participants using gas chromatography combined with ion mobility spectrometry (GC-IMS, G.A.S. mbH). Polymerase chain reaction (PCR) tests combined with clinical diagnostics were used as a reference method. Once the ML training-testing cycle is completed, the diagnostics of a single data set may be completed in as little as a few seconds. A convincing difference in the Volatile Organic Compounds (VOS) breath biomarker signature with COVID-19 positive and negative participants has been found. This approach is a generic method that can be applied to data sets collected with other types of hardware, e.g., different settings of GC, GC-MS, GC-GC, or other methods providing high information density in 2-dimensional data sets.
Keywords
ML algorithm; pattern recognition; Gas chromatography-ion mobility; COVID-19 volatile biomarkers
Subject
Biology and Life Sciences, Other
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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