<?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>2007-1523</journal-id>
<journal-title><![CDATA[Revista mexicana de trastornos alimentarios]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Mex. de trastor. aliment]]></abbrev-journal-title>
<issn>2007-1523</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Estudios Superiores Iztacala]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-15232022000100061</article-id>
<article-id pub-id-type="doi">10.22201/fesi.20071523e.2022.1.718</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo de redes neuronales artificiales: Variables neuropsicológicas y su relación con el porcentaje de grasa corporal en adultos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Méndez-Peña]]></surname>
<given-names><![CDATA[Bárbara Itzel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Murillo-Tovar]]></surname>
<given-names><![CDATA[María Magdalena]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Leija-Alva]]></surname>
<given-names><![CDATA[Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Montufar Burgos]]></surname>
<given-names><![CDATA[Itzihuari Iratzi]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Serena-Alvarado]]></surname>
<given-names><![CDATA[Angélica]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Durán-Arciniega]]></surname>
<given-names><![CDATA[Roxana Sarai]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez-Vielma]]></surname>
<given-names><![CDATA[Nadia Mabel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguilera-Sosa]]></surname>
<given-names><![CDATA[Víctor Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional Centro Interdisciplinario de Ciencias de la Salud ]]></institution>
<addr-line><![CDATA[CDMX ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional Departamento de Investigación y Posgrado ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>1</numero>
<fpage>61</fpage>
<lpage>70</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-15232022000100061&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-15232022000100061&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-15232022000100061&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract There is a growing interest to understand the neural functions and substrates of complex cognitive processes related to Obesity (OB). Artificial Intelligence (AI) is being applied, specifically the perceptron model of Artificial Neural Networks (ANN) in non-communicable chronic diseases, to identify with greater certainty the connective factors (synaptic networks) between the input variables and the output variables associated.  Objective Identify the synaptic weights of the ANN whose input variables are the executive functions (EF) and healthy lifestyles as predictors of Body Fat Percentage (BFP) in a group of adult subjects with different levels of BFP.  Methods It was an exploratory, quantitative, cross-sectional, comparative, convenience, and explanatory research. The Neuropsychological Battery (BANFE-2) and the Overeating Questionnaire (OQ) were administered to 40 participants aged between 18-38 years. BFP was measured using a RENPHO ES-24M Smart Body Composition Scale. The perceptron ANN model with ten trials was applied with a multilayer-perceptron.  Results The ANN showed that the sensory variables with greater synaptic weight for BFP were Stroop A and B Errors and Successes of BANFE-2, and OQ scales Rationalizations and Healthy Habits.  Conclusions ANN proved to be important in the simultaneous analysis of neuropsychological and healthy lifestyle data for the analysis of OB prevention and treatment by identifying the variables that are closely related. These findings open the door for the use of non-linear analysis models, which allow the identification of relationships of different weights, between input and output variables, to more effectively direct interventions to modify obesity habits.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Existe un interés creciente por comprender las funciones neuronales y sustratos cognitivos complejos relacionados con la obesidad. Se está aplicando Inteligencia Artificial, en concreto el modelo perceptrón de Redes Neuronales Artificiales en enfermedades crónicas no transmisibles, para identificar con mayor certeza los factores de conexión (redes sinápticas) entre las variables de entrada y las variables de salida.  Objetivo Identificar pesos sinápticos de la RNA cuyas variables de entrada fueron las funciones ejecutivas y los estilos de vida saludable, como predictores del Porcentaje de Grasa Corporal en un grupo de sujetos adultos con diferentes niveles del Porcentaje de Grasa.  Métodos  se trató de una investigación exploratoria, cuantitativa, transversal, comparativa, de conveniencia y explicativa. Se administró la Batería Neuropsicológica (BANFE-2) y el Cuestionario de Sobreingesta (OQ), a 40 participantes con edades comprendidas entre los 18-38 años. El porcentaje de grasa se midió con una báscula de composición corporal (RENPHO ES-24M). El modelo redes neuronales de perceptrón, se ejecutó con diez ensayos.  Resultados El modelo de Red Neuronal mostró que las variables sensoriales con mayor peso sináptico para el porcentaje de grasa, fueron Errores Stroop A y B y Aciertos de BANFE-2, y Racionalizaciones de las escalas OQ y Hábitos Saludables.  Conclusiones  las redes neuronales artificiales demostró ser importante en el análisis simultáneo de datos neuropsicológicos y de estilo de vida saludable para el análisis de prevención y tratamiento de la obesidad, al identificar las variables que están estrechamente relacionadas. Estos hallazgos abren la puerta al uso de modelos de análisis no lineales, que permiten identificar relaciones de diferente peso, entre variables de entrada y salida, más eficientes que los modelos lineales.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[healthy habits]]></kwd>
<kwd lng="en"><![CDATA[neuropsychological variables]]></kwd>
<kwd lng="en"><![CDATA[body fat]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="es"><![CDATA[hábitos saludables]]></kwd>
<kwd lng="es"><![CDATA[variables neuropsicológicas]]></kwd>
<kwd lng="es"><![CDATA[grasa corporal]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
</kwd-group>
</article-meta>
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