<?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>S0188-95322023000400053</article-id>
<article-id pub-id-type="doi">10.17488/rmib.44.4.4</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Sistema de Cribado Primario para la Sarcopenia en Personas Adultas Mayores Basado en Inteligencia Artificial]]></article-title>
<article-title xml:lang="en"><![CDATA[Primary Screening System for Sarcopenia in Elderly People Based on Artificial Intelligence]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arceo-Diaz]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bricio-Barrios]]></surname>
<given-names><![CDATA[Elena Elsa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Trujillo-Trujillo]]></surname>
<given-names><![CDATA[Xochitl Angélica Rosio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Farias]]></surname>
<given-names><![CDATA[José Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bricio-Barrios]]></surname>
<given-names><![CDATA[Jaime Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rios-Silva]]></surname>
<given-names><![CDATA[Mónica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huerta-Viera]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Colima  ]]></institution>
<addr-line><![CDATA[ Colima]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México  ]]></institution>
<addr-line><![CDATA[ Colima]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>44</volume>
<numero>spe1</numero>
<fpage>53</fpage>
<lpage>69</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-95322023000400053&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-95322023000400053&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-95322023000400053&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Este estudio propone un sistema de cribado primario para diagnosticar la sarcopenia en adultos mayores a través de medidas antropométricas. Esta investigación exploratoria involucró inicialmente a 150 personas de edad avanzada, de las cuales 122 fueron seleccionadas después de un proceso de depuración de datos. Empleando técnicas de aprendizaje automático como el agrupamiento jerárquico y los árboles de decisión, se redujeron las 13 medidas antropométricas originales a cinco características clave. Se crearon tres sistemas de clasificación: el primero basado en parámetros previamente establecidos (masa muscular apendicular, velocidad de marcha y fuerza de agarre); el segundo consideró medidas de las extremidades superiores (masa muscular promedio de ambos brazos, fuerza de agarre, velocidad de marcha y porcentaje de grasa corporal); y el tercero se centró en las medidas de las extremidades inferiores (masa muscular promedio de ambas piernas, fuerza de agarre, velocidad de marcha y porcentaje de grasa corporal). Estos sistemas de clasificación se validaron clínicamente en un grupo de 57 pacientes previamente diagnosticados por especialistas, de los cuales 10 recibieron un diagnóstico positivo de sarcopenia. Los resultados mostraron eficiencias similares en los tres sistemas, con ocho de los diez diagnósticos positivos conocidos clasificados en el mismo grupo. Además, el estudio proporciona puntos de corte específicos para cada sistema, facilitando así el diagnóstico clínico de la sarcopenia por parte de profesionales médicos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract This study proposes a primary screening system for diagnosing sarcopenia in older adults through anthropometric measures. This exploratory research initially involved 150 elderly individuals, of whom 122 were selected after a data purification process. Using machine learning techniques such as hierarchical clustering and decision trees, the original set of 13 anthropometric measures was reduced to five key features. Three classification systems were created: the first based on previously established parameters (appendicular muscle mass, walking speed, and grip strength); the second considered upper limb measures (average muscle mass of both arms, grip strength, walking speed, and body fat percentage); and the third focused on lower limb measures (average muscle mass of both legs, grip strength, walking speed, and body fat percentage). These classification systems were clinically validated in a group of 57 patients previously diagnosed by specialists, of which 10 received a positive sarcopenia diagnosis. The results showed similar efficiencies in all three systems, with eight of the ten known positive diagnoses classified in the same group. Additionally, the study provides specific cut-off points for each system, thus facilitating the clinical diagnosis of sarcopenia by medical professionals.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[clustering jerárquico]]></kwd>
<kwd lng="es"><![CDATA[medidas antropométricas]]></kwd>
<kwd lng="es"><![CDATA[personas adultas mayores]]></kwd>
<kwd lng="es"><![CDATA[Random Forest]]></kwd>
<kwd lng="es"><![CDATA[sarcopenia]]></kwd>
<kwd lng="en"><![CDATA[anthropometric measures]]></kwd>
<kwd lng="en"><![CDATA[elderly people]]></kwd>
<kwd lng="en"><![CDATA[hierarchical clustering]]></kwd>
<kwd lng="en"><![CDATA[Random Forest]]></kwd>
<kwd lng="en"><![CDATA[sarcopenia]]></kwd>
</kwd-group>
</article-meta>
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