<?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>2072-8174</journal-id>
<journal-title><![CDATA[Revista del Nacional (Itauguá)]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Nac. (Itauguá)]]></abbrev-journal-title>
<issn>2072-8174</issn>
<publisher>
<publisher-name><![CDATA[Hospital Nacional (Itauguá)]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2072-81742025000100105</article-id>
<article-id pub-id-type="doi">10.18004/rdn2025.e1700105</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Entrenamiento de una red neuronal para estimación del modelo homeostático de resistencia a la insulina]]></article-title>
<article-title xml:lang="en"><![CDATA[Training a neural network for estimating the homeostatic model of insulin resistance]]></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  ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>17</volume>
<fpage>105</fpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_arttext&amp;pid=S2072-81742025000100105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_abstract&amp;pid=S2072-81742025000100105&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_pdf&amp;pid=S2072-81742025000100105&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  la evaluación del modelo homeostático es una prueba muy cercana al estándar de oro (pinza euglucémica).  Objetivo:  entrenar una red neuronal tipo perceptrón multicapa para determinar el modelo homeostático de resistencia a la insulina.  Metodología:  estudio analítico y transversal. El aprendizaje de la red neuronal se realizó a partir de una base de datos de 2004 adultos venezolanos. Posteriormente se añadió 4363 adultos mexicanos de la base de datos de la Encuesta Nacional de Salud y Nutrición (ENSANUT). Las variables fueron modelo homeostático de resistencia a la insulina (HOMA2-IR), insulina basal, glucosa basal. Se empleó redes neuronales tipo perceptrón multicapa.  Resultados:  el entrenamiento del modelo de redes neuronales tuvo un error relativo de 0,003, mientras que en la prueba fue 0,005. Para HOMA2-IR cualitativo, el porcentaje de pronósticos incorrectos fue 0,60 % en el entrenamiento, y 0,70 % en la prueba. Después del aprendizaje del modelo, se añadió valores de insulina y glucosa basal de 4363 adultos mexicanos, observándose que los valores de HOMA2-IR generados mediante perceptrón multicapa mantuvieron la eficiencia del modelo, obteniéndose un coeficiente de determinación R2 de 0,983, lo que implica que el 98 % de la variación en los valores de HOMA2-IR pueden ser explicados mediante valores de HOMA2-IR obtenidos mediante perceptrón multicapa.  Conclusiones:  la red neuronal tipo perceptrón multicapa, da resultados virtualmente idénticos a los obtenidos mediante la calculadora HOMA2-IR. La implementación de este algoritmo puede ser beneficioso como herramienta de fácil implementación en los sistemas de atención primaria, especializada y en entornos hospitalarios.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  the evaluation of the homeostatic model is a test very close to the gold standard (euglycemic clamp).  Objective:  train a multilayer perceptron-type neural network to determine the homeostatic model of insulin resistance.  Methodology:  analytical and cross-sectional study. The learning of the neural network was carried out from a database of 2004 Venezuelan adults. Subsequently, 4,363 Mexican adults were added to the database of the National Health and Nutrition Survey (ENSANUT). The variables were homeostatic model of insulin resistance (HOMA2-IR), basal insulin, and basal glucose. Multilayer perceptron-type neural networks were used.  Results:  the training of the neural network model had a relative error of 0.003, while in the test it was 0.005. For qualitative HOMA2-IR, the percentage of incorrect predictions was 0.60 % in training, and 0.70 % in testing. After learning the model, insulin and basal glucose values from 4363 Mexican adults were added, observing that the HOMA2-IR values generated by multilayer perceptron maintained the efficiency of the model, obtaining a coefficient of determination R2 of 0.983, which implies that 98 % of the variation in HOMA2-IR values can be explained by HOMA2-IR values obtained using multilayer perceptron.  Conclusions:  the multilayer perceptron-type neural network gives results virtually identical to those obtained using the HOMA2-IR calculator. The implementation of this algorithm can be beneficial as a tool that is easy to implement in primary and specialized care systems and in hospital environments.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[resistencia a la Insulina, glucemia]]></kwd>
<kwd lng="es"><![CDATA[redes neurales de la computación, toma de decisiones asistida por computador, diagnóstico por computador.]]></kwd>
<kwd lng="en"><![CDATA[Insulin Resistance]]></kwd>
<kwd lng="en"><![CDATA[Glicemia]]></kwd>
<kwd lng="en"><![CDATA[Neural Networks, Computer]]></kwd>
<kwd lng="en"><![CDATA[Decision Making, Computer-Assisted]]></kwd>
<kwd lng="en"><![CDATA[Diagnosis, Computer-Assisted]]></kwd>
</kwd-group>
</article-meta>
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