A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning

Soto, I ORCID: https://orcid.org/0000-0002-5501-5651, Zamorano-Illanes, R ORCID: https://orcid.org/0000-0002-3995-697X, Becerra, R ORCID: https://orcid.org/0000-0003-1590-9877, Játiva, PP ORCID: https://orcid.org/0000-0002-3958-503X, Azurdia-Meza, CA ORCID: https://orcid.org/0000-0003-3461-4484, Alavia, W ORCID: https://orcid.org/0000-0001-5674-8489, García, V ORCID: https://orcid.org/0000-0002-5312-925X, Ijaz, M ORCID: https://orcid.org/0000-0002-0050-9435 and Zabala-Blanco, D ORCID: https://orcid.org/0000-0002-5692-5673 (2023) A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning. Sensors, 23 (3). p. 1533. ISSN 1424-8220

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Abstract

This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation (Formula presented.) yields a greater profit. Performance studies indicate that, for BER = (Formula presented.), there are gains of −10 [dB], −3 [dB], 3 [dB], and 5 [dB] for (Formula presented.), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of (Formula presented.), greater than that of the other models, and a (Formula presented.) of (Formula presented.) for positive values.