<?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>0864-2125</journal-id>
<journal-title><![CDATA[Revista Cubana de Medicina General Integral]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Cubana Med Gen Integr]]></abbrev-journal-title>
<issn>0864-2125</issn>
<publisher>
<publisher-name><![CDATA[ECIMED]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0864-21252023000300015</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo de enfermedad cardiovascular basado en inteligencia artificial en la atención primaria de salud]]></article-title>
<article-title xml:lang="en"><![CDATA[A Predictive Model for Cardiovascular Disease based on Artificial Intelligence in Primary Health Care]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Vega Abascal]]></surname>
<given-names><![CDATA[Jorge Baudilio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Piriz Assa]]></surname>
<given-names><![CDATA[Alberto Rubén]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nápoles Riaño]]></surname>
<given-names><![CDATA[Diego]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciencias Médicas Holguín Policlínico Docente &#8220;José Ávila Serrano&#8221; ]]></institution>
<addr-line><![CDATA[Velasco Holguín]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hospital Pediátrico Provincial &#8220;Octavio de la Concepción de la Pedraja&#8221;  ]]></institution>
<addr-line><![CDATA[ Holguín]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Ciencias Médicas Holguín  ]]></institution>
<addr-line><![CDATA[ Holguín]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2023</year>
</pub-date>
<volume>39</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S0864-21252023000300015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S0864-21252023000300015&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S0864-21252023000300015&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  En Cuba y en el resto del mundo, las enfermedades cardiovasculares son reconocidas como un problema de salud pública mayúsculo y creciente, que provoca una alta mortalidad.  Objetivo:  Diseñar un modelo predictivo para estimar el riesgo de enfermedad cardiovascular basado en técnicas de inteligencia artificial.  Métodos:  La fuente de datos fue una cohorte prospectiva que incluyó 1633 pacientes, seguidos durante 10 años, fue utilizada la herramienta de minería de datos Weka, se emplearon técnicas de selección de atributos para obtener un subconjunto más reducido de variables significativas, para generar los modelos fueron aplicados: el algoritmo de reglas JRip y el meta algoritmo Attribute Selected Classifier, usando como clasificadores el J48 y el Multilayer Perceptron. Se compararon los modelos obtenidos y se aplicaron las métricas más usadas para clases desbalanceadas.  Resultados:  El atributo más significativo fue el antecedente de hipertensión arterial, seguido por el colesterol de lipoproteínas de alta densidad y de baja densidad, la proteína c reactiva de alta sensibilidad y la tensión arterial sistólica, de estos atributos se derivaron todas las reglas de predicción, los algoritmos fueron efectivos para generar el modelo, el mejor desempeño fue con el Multilayer Perceptron, con una tasa de verdaderos positivos del 95,2 % y un área bajo la curva ROC de 0,987 en la validación cruzada.  Conclusiones:  Fue diseñado un modelo predictivo mediante técnicas de inteligencia artificial, lo que constituye un valioso recurso orientado a la prevención de las enfermedades cardiovasculares en la atención primaria de salud.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  In Cuba and in the rest of the world, cardiovascular diseases are recognized as a major and growing public health problem, which causes high mortality.  Objective:  To design a predictive model to estimate the risk of cardiovascular disease based on artificial intelligence techniques.  Methods:  The data source was a prospective cohort including 1633 patients, followed for 10 years. The data mining tool Weka was used and attribute selection techniques were employed to obtain a smaller subset of significant variables. To generate the models, the rule algorithm JRip and the meta-algorithm Attribute Selected Classifier were applied, using J48 and Multilayer Perceptron as classifiers. The obtained models were compared and the most used metrics for unbalanced classes were applied.  Results:  The most significant attribute was history of arterial hypertension, followed by high and low density lipoprotein cholesterol, high sensitivity c-reactive protein and systolic blood pressure; all the prediction rules were derived from these attributes. The algorithms were effective to generate the model. The best performance was obtained using the Multilayer Perceptron, with a true positive rate of 95.2% and an area under the ROC curve of 0.987 in the cross validation.  Conclusions:  A predictive model was designed using artificial intelligence techniques; it is a valuable resource oriented to the prevention of cardiovascular diseases in primary health care.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[enfermedad cardiovascular]]></kwd>
<kwd lng="es"><![CDATA[factores de riesgo]]></kwd>
<kwd lng="es"><![CDATA[modelo predictivo]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[atención primaria de salud]]></kwd>
<kwd lng="en"><![CDATA[cardiovascular disease]]></kwd>
<kwd lng="en"><![CDATA[risk factors]]></kwd>
<kwd lng="en"><![CDATA[predictive model]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[automated learning]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[primary health care]]></kwd>
</kwd-group>
</article-meta>
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