<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-9532</journal-id>
<journal-title><![CDATA[Revista mexicana de ingeniería biomédica]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. mex. ing. bioméd]]></abbrev-journal-title>
<issn>0188-9532</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Mexicana de Ingeniería Biomédica]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-95322022000300104</article-id>
<article-id pub-id-type="doi">10.17488/rmib.43.3.5</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Detection of People Positive to COVID-19 through ATR-FTIR Spectra Analysis of Saliva using Machine Learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Detección de Personas Positivas a COVID-19 mediante el Análisis de Espectros ATR-FTIR de Muestras de Saliva empleando Machine Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vazquez-Zapien]]></surname>
<given-names><![CDATA[Gustavo Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Mata-Miranda]]></surname>
<given-names><![CDATA[Monica Maribel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Martinez-Cuazitl]]></surname>
<given-names><![CDATA[Adriana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerrero-Ruiz]]></surname>
<given-names><![CDATA[Melissa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Garibay-Gonzalez]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sanchez-Brito]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Secretaría de la Defensa Nacional Centro Militar de Ciencias de la Salud Escuela Militar de Medicina]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnicno Nacional Escuela Superior de Cómputo ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>43</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322022000300104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322022000300104&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322022000300104&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. This virus's spread is mainly through droplets released from the nose or mouth of an infected person. Although vaccines have been developed that effectively reduce the effects that this viral infection causes, the most effective method to contain the virus&#8217;s spread is numerous tests to detect and isolate possible carriers. However, the response time, combined with the cost of actual tests, makes this option impractical. Herein, we compare some machine learning methodologies to propose a reliable strategy to detect people positive to COVID-19, analyzing saliva spectra obtained by Fourier transform infrared (FTIR) spectroscopy. After analyzing 1275 spectra, with 7 strategies commonly used in machine learning, we concluded that a multivariate linear regression model (MLMR) turns out to be the best option to identify possible infected persons. According to our results, the displacement observed in the region of the amide I of the spectrum, is fundamental and reliable to establish a border from the change in slope that causes this displacement that allows us to characterize the carriers of the virus. Being more agile and cheaper than reverse transcriptase polymerase chain reaction (RT-PCR), it could be reliably applied as a preliminary strategy to RT-PCR.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La COVID-19 es una enfermedad infecciosa ocasionada por el virus SARS-CoV-2. La propagación de este virus se produce principalmente a través de gotitas liberadas por la nariz o la boca de una persona infectada. Aunque se han desarrollado vacunas que permiten reducir efectivamente los efectos que esta infección viral provoca, el método más eficaz para contener la propagación del virus son las numerosas pruebas para detectar y aislar los posibles portadores. Sin embargo, el tiempo de respuesta, combinado con el costo de las pruebas reales, hace que esta opción sea poco práctica. Aquí, comparamos algunas metodologías de machine learning para proponer una estrategia confiable para detectar personas positivas a COVID-19 analizando espectros de saliva obtenidos por espectroscopia infrarroja transformada de Fourier (FTIR). Tras analizar 1275 espectros, con 7 estrategias comúnmente empleadas en el área de machine learning, concluimos que un modelo de regresión lineal multivariante (MLMR) resulta ser la mejor opción para identificar posibles infectados. De acuerdo con nuestros resultados, el desplazamiento observado en la región de la amida I del espectro, resulta fundamental y confiable para establecer una frontera a partir del cambio de pendiente que este provoca. Al ser más ágil y económica que la reacción en cadena de la polimerasa con transcriptasa inversa (RT-PCR), podría aplicarse confiablemente como estrategia preliminar a RT-PCR.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Saliva]]></kwd>
<kwd lng="en"><![CDATA[ATR-FTIR]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[COVID-19]]></kwd>
<kwd lng="en"><![CDATA[diagnosis]]></kwd>
<kwd lng="es"><![CDATA[Saliva]]></kwd>
<kwd lng="es"><![CDATA[ATR-FTIR]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje máquina]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico]]></kwd>
</kwd-group>
</article-meta>
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