<?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>0123-7799</journal-id>
<journal-title><![CDATA[TecnoLógicas]]></journal-title>
<abbrev-journal-title><![CDATA[TecnoL.]]></abbrev-journal-title>
<issn>0123-7799</issn>
<publisher>
<publisher-name><![CDATA[Instituto Tecnológico Metropolitano - ITM]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-77992024000200208</article-id>
<article-id pub-id-type="doi">10.22430/22565337.3061</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Implementación de mapas cognitivos difusos con algoritmos genéticos para predecir diabetes mellitus tipo 2]]></article-title>
<article-title xml:lang="en"><![CDATA[Implementation of Fuzzy Cognitive Maps with Genetic Algorithms for Predicting Type 2 Diabetes Mellitus]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoyos]]></surname>
<given-names><![CDATA[William]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ruíz]]></surname>
<given-names><![CDATA[Rander]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoyos]]></surname>
<given-names><![CDATA[Kenia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Cooperativa de Colombia  ]]></institution>
<addr-line><![CDATA[Montería ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Antioquia  ]]></institution>
<addr-line><![CDATA[Caucasia ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Clínica Salud Social Laboratorio Clínico Humano ]]></institution>
<addr-line><![CDATA[Sincelejo ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2024</year>
</pub-date>
<volume>27</volume>
<numero>60</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-77992024000200208&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-77992024000200208&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-77992024000200208&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La diabetes mellitus tipo 2 es una enfermedad crónica no transmisible, causada por un trastorno en el metabolismo de la glucosa, que provoca un aumento anormal de su concentración en la sangre. El diagnóstico tardío de esta enfermedad contribuye al aumento de las tasas de morbilidad y mortalidad a nivel mundial. El desarrollo de modelos basados en inteligencia artificial para la predicción de diabetes podría acelerar el diagnóstico. Por tanto, el objetivo del presente estudio fue implementar un modelo de predicción de diabetes mellitus tipo 2 basado en mapas cognitivos difusos entrenado con un algoritmo genético. La metodología empleada consistió en utilizar un conjunto de datos del Instituto Nacional de Diabetes y Enfermedades Digestivas y Renales de la población de indios PIMA, que contiene información demográfica y clínica de 768 pacientes. El 70 % de los datos se empleó para el entrenamiento y validación, y el 30 % restante se utilizó para las pruebas de rendimiento. El modelo de mapas cognitivos difusos puede predecir la enfermedad con un 99 % de exactitud, 98 % de precisión y recall de 100 %. Se concluye que el modelo presenta una buena capacidad para predecir y evaluar el comportamiento de las variables de interés en la diabetes mellitus tipo 2, mostrando su valor como herramienta de soporte en la identificación oportuna de la enfermedad y apoyo a la toma de decisiones por parte del profesional médico.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Type 2 diabetes mellitus is a chronic non-communicable disease caused by a disorder in glucose metabolism, which results in an abnormal increase in glucose concentration in the blood. The late diagnosis of this disease contributes to the increased worldwide rates of morbidity and mortality. The development of models based on artificial intelligence for the prediction of diabetes could accelerate diagnosis. Therefore, the aim of the present study was to develop a prediction model for type 2 diabetes mellitus based on fuzzy cognitive maps trained with a genetic algorithm. The methodology employed consisted of using a dataset from the National Institute of Diabetes and Digestive and Kidney Diseases PIMA Indian population, which contains demographic and clinical information from 768 patients. For training and validation, 70 % of the data was used and the remaining 30 % was used for performance testing. The fuzzy cognitive map model can predict the disease with 99 % accuracy, 98 % precision, and 100 % recall. It is concluded that the model presents a good ability to predict and evaluate the behavior of the variables of interest in type 2 diabetes mellitus, showing its value as a support tool for the timely identification of the disease and support for decision making by the physician.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Diabetes Mellitus Tipo 2]]></kwd>
<kwd lng="es"><![CDATA[mapas cognitivos difusos]]></kwd>
<kwd lng="es"><![CDATA[factores de riesgo]]></kwd>
<kwd lng="es"><![CDATA[algoritmos de predicción]]></kwd>
<kwd lng="es"><![CDATA[algoritmos genéticos]]></kwd>
<kwd lng="en"><![CDATA[Type 2 Diabetes Mellitus]]></kwd>
<kwd lng="en"><![CDATA[fuzzy cognitive maps]]></kwd>
<kwd lng="en"><![CDATA[risk factors]]></kwd>
<kwd lng="en"><![CDATA[prediction algorithms]]></kwd>
<kwd lng="en"><![CDATA[genetic algorithms]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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