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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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>=</mo><msup><mn>2</mn><msup><mn>2</mn><mi>i</mi></msup></msup><mo>×</mo><msup><mn>2</mn><msup><mn>2</mn><mi>i</mi></msup></msup><mo>,</mo><mrow><mo>(</mo><mi>i</mi><mo>=</mo><mn>3</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula> yields a greater profit. Performance studies indicate that, for BER = <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>10</mn><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, there are gains of −10 [dB], −3 [dB], 3 [dB], and 5 [dB] for <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>N</mi><mo>=</mo><msup><mn>2</mn><msup><mn>2</mn><mi>i</mi></msup></msup><mo>×</mo><msup><mn>2</mn><msup><mn>2</mn><mi>i</mi></msup></msup><mo>,</mo><mrow><mo>(</mo><mi>i</mi><mo>=</mo><mn>0</mn><mo>,</mo><mn>1</mn><mo>,</mo><mn>2</mn><mo>,</mo><mn>3</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>96.03</mn><mo>%</mo></mrow></semantics></math></inline-formula>, greater than that of the other models, and a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></semantics></math></inline-formula> of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula> for positive values.