<?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>0123-3033</journal-id>
<journal-title><![CDATA[Ingeniería y competitividad]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. compet.]]></abbrev-journal-title>
<issn>0123-3033</issn>
<publisher>
<publisher-name><![CDATA[Facultad de Ingeniería, Universidad del Valle]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0123-30332024000100001</article-id>
<article-id pub-id-type="doi">10.25100/iyc.v26i1.13229</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Cardiovascular Risk Prediction through Machine Learning: A Comparative Analysis of Techniques]]></article-title>
<article-title xml:lang="es"><![CDATA[Predicción de Riesgo Cardiovascular mediante Aprendizaje Automático: Un Análisis Comparativo entre Técnicas]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Arrubla-Hoyos]]></surname>
<given-names><![CDATA[Wilson]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carrascal-Porras]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Abierta y a Distancia Escuela de ciencias Básica tecnología e ingeniería ]]></institution>
<addr-line><![CDATA[Corozal ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Córdoba Facultad de ingeniería Departamento de Ingeniería de Sistemas y Telecomunicaciones]]></institution>
<addr-line><![CDATA[Montería ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2024</year>
</pub-date>
<volume>26</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0123-30332024000100001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0123-30332024000100001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0123-30332024000100001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The field of healthcare, driven by the continuous growth of data related to human health and the ongoing course of digital transformation, is undergoing a significant evolution. In this experimental study, a comparison of Artificial Intelligence techniques, specifically neural networks, Random Forest, and decision tree, was conducted to evaluate their effectiveness in diagnosing cardiovascular diseases. This was achieved by leveraging clinical data available in open-access databases. The methodology focused on identifying the most influential variables in cardiovascular disease diagnosis through a comprehensive literature review. Subsequently, the Machine Learning techniques to be employed were determined, and the most suitable dataset for these variables was acquired. The results revealed that all three Artificial Intelligence techniques demonstrated good performance in diagnosing cardiovascular diseases. It is worth highlighting that the neural network-based model excelled with an accuracy of 89%, establishing itself as a highly relevant tool for supporting timely disease diagnosis. These findings suggest a potential positive impact on clinical practice and future healthcare by providing healthcare professionals with a valuable resource for making informed decisions in the diagnosis and treatment of cardiovascular diseases. Ultimately, this could enhance the quality of patient care and their overall well-being. This study reinforces the notion that Machine Learning techniques play a crucial role in transforming healthcare and clinical decision-making in the field of health, offering new perspectives for the prevention and treatment of cardiovascular diseases and other medical disorders.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El ámbito de la atención médica, impulsado por el crecimiento constante de datos relacionados con la salud humana y el curso en desarrollo de la transformación digital, está experimentando una notable evolución. En este estudio de carácter experimental, se llevó a cabo una comparativa de técnicas de Inteligencia Artificial, específicamente redes neuronales, Random Forest y árbol de decisión, con el propósito de evaluar su eficacia en el diagnóstico de enfermedades cardiovasculares. Esto se logró aprovechando datos clínicos disponibles en bases de datos de acceso abierto. La metodología se enfocó en la identificación de las variables más influyentes en el diagnóstico de enfermedades cardiovasculares mediante una revisión exhaustiva de la literatura. Luego, se determinaron las técnicas de Aprendizaje automático a emplear y se adquirió el conjunto de datos más apropiado para estas variables. Los resultados revelaron que las tres técnicas de Inteligencia Artificial demostraron un buen desempeño en el diagnóstico de enfermedades cardiovasculares. Es importante resaltar que el modelo basado en redes neuronales destacó con una precisión del 89%, consolidándose como una herramienta de gran relevancia para respaldar el diagnóstico oportuno de estas enfermedades. Estos hallazgos sugieren un posible impacto positivo en la práctica clínica y la atención médica futura al proporcionar a los profesionales de la salud un recurso valioso para tomar decisiones informadas en el diagnóstico y tratamiento de enfermedades cardiovasculares. En última instancia, esto podría mejorar la calidad de la atención y la vida de los pacientes. Este estudio refuerza la noción de que las técnicas de Aprendizaje automático desempeñan un rol fundamental en la transformación de la atención médica y la toma de decisiones clínicas en el ámbito de la salud, ofreciendo nuevas perspectivas para la prevención y el tratamiento de enfermedades cardiovasculares y otros trastornos médicos.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[cardiovascular disease]]></kwd>
<kwd lng="en"><![CDATA[big data]]></kwd>
<kwd lng="en"><![CDATA[random forest]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[decision tree]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia Artificial]]></kwd>
<kwd lng="es"><![CDATA[enfermedad cardiovascular]]></kwd>
<kwd lng="es"><![CDATA[random forest]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[árbol de decisión]]></kwd>
</kwd-group>
</article-meta>
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