<?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>0016-3813</journal-id>
<journal-title><![CDATA[Gaceta médica de México]]></journal-title>
<abbrev-journal-title><![CDATA[Gac. Méd. Méx]]></abbrev-journal-title>
<issn>0016-3813</issn>
<publisher>
<publisher-name><![CDATA[Academia Nacional de Medicina de México A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0016-38132025000300013</article-id>
<article-id pub-id-type="doi">10.24875/gmm.24000411</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Implementación de redes neuronales para la predicción del síndrome metabólico: un estudio con datos multinacionales]]></article-title>
<article-title xml:lang="en"><![CDATA[Implementation of neural networks for predicting metabolic syndrome: a multinational data study]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guevara-Tirado]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Científica del Sur Facultad de Medicina Humana ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Perú</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>161</volume>
<numero>3</numero>
<fpage>334</fpage>
<lpage>341</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0016-38132025000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0016-38132025000300013&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0016-38132025000300013&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes: El síndrome metabólico (SM) está constituido por anomalías como la obesidad central, la resistencia a la insulina, la hipertensión y la dislipidemia.  Objetivo: Implementar una red neuronal para predecir el SM a partir del colesterol, los triglicéridos, el colesterol unido a lipoproteínas de alta densidad (HDL), la obesidad y la hipertensión.  Material y métodos: Estudio analítico y transversal con 1878 pacientes de bases de datos de Venezuela, Tailandia e Indonesia. Se incluyeron las variables SM, hipertensión, obesidad, HDL, triglicéridos y colesterol total. Se usaron redes neuronales tipo perceptrón multicapa, evaluadas con tablas de clasificación, área bajo la curva (AUC) y métricas de desempeño (sensibilidad, especificidad y valores predictivos positivo y negativo).  Resultados: La red neuronal mostró una alta capacidad para predecir el SM, con un bajo porcentaje de pronósticos incorrectos tanto en el conjunto de entrenamiento (15.80%) como en el de prueba (18.20%). En el entrenamiento, la precisión global fue del 84.20%, con mayor precisión para casos sin SM (88.30%) que para casos con SM (77.80%). En las pruebas, la precisión global fue del 81.80%, también con mayor precisión para casos sin SM (86.60%) que con SM (74.80%). El AUC fue 0.911, indicando una capacidad predictiva sobresaliente. Respecto al desempeño del modelo, la sensibilidad fue del 81.25% en el entrenamiento y del 79.26% en la prueba, mientras que la especificidad alcanzó el 85.92% y el 83.33%, respectivamente. El valor predictivo positivo fue del 77.80% en entrenamiento y del 74.78% en prueba, y el valor predictivo negativo del 88.30% y el 86.57%, respectivamente.  Conclusiones: La red neuronal tipo perceptrón multicapa es una herramienta eficaz para predecir el SM, mostrando una capacidad predictiva sobresaliente.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background: Metabolic syndrome (MS) is composed of abnormalities such as central obesity, insulin resistance, hypertension and dyslipidemia.  Objective: To implement a neural network to predict MS from cholesterol, triglycerides, high density lipoproteins (HDL), obesity and hypertension.  Material and methods: Analytical and cross-sectional study with 1878 patients from databases in Venezuela, Thailand and Indonesia. Variables such as MS, hypertension, obesity, HDL, triglycerides and total cholesterol were included. Multilayer perceptron neural networks were used, evaluated with classification tables, area under the curve (AUC) and performance metrics (sensitivity, specificity, positive and negative predictive values).  Results: The neural network showed a high capacity to predict MS, with a low percentage of incorrect predictions both in the training set (15.80%) and in the test set (18.20%). In training, the overall accuracy was 84.20%, with higher accuracy for cases without MS (88.30%) than for cases with MS (77.80%). In testing, the overall accuracy was 81.80%, also with higher accuracy for cases without MS (86.60%) than for cases with MS (74.80%). The AUC was 0.911, indicating an outstanding predictive capacity. Regarding the model performance, the sensitivity was 81.25% in training and 79.26% in testing, while the specificity reached 85.92% and 83.33%, respectively. The positive predictive value was 77.80% in training and 74.78% in testing, and the negative predictive value was 88.30% and 86.57%, respectively.  Conclusions: The multilayer perceptron neural network is an effective tool to predict MS, showing an outstanding predictive capacity.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Síndrome metabólico]]></kwd>
<kwd lng="es"><![CDATA[Registros médicos]]></kwd>
<kwd lng="es"><![CDATA[Lípidos]]></kwd>
<kwd lng="es"><![CDATA[Redes neurales de la computación]]></kwd>
<kwd lng="es"><![CDATA[Toma de decisiones asistida por computador]]></kwd>
<kwd lng="en"><![CDATA[Metabolic syndrome]]></kwd>
<kwd lng="en"><![CDATA[Medical records]]></kwd>
<kwd lng="en"><![CDATA[Lipids]]></kwd>
<kwd lng="en"><![CDATA[Neural networks computer]]></kwd>
<kwd lng="en"><![CDATA[Decision making by computer-assisted]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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<name>
<surname><![CDATA[Gutiérrez-Esparza]]></surname>
<given-names><![CDATA[GO]]></given-names>
</name>
<name>
<surname><![CDATA[Ramírez-del Real]]></surname>
<given-names><![CDATA[TA]]></given-names>
</name>
<name>
<surname><![CDATA[Martínez-García]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Infante Vázquez]]></surname>
<given-names><![CDATA[O]]></given-names>
</name>
<name>
<surname><![CDATA[Vallejo]]></surname>
<given-names><![CDATA[M]]></given-names>
</name>
<name>
<surname><![CDATA[Hernández-Torruco]]></surname>
<given-names><![CDATA[J]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine and deep learning applied to predict metabolic syndrome without a blood screening]]></article-title>
<source><![CDATA[Appl Sci (Basel)]]></source>
<year>2021</year>
<volume>11</volume>
<page-range>4334</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
