<?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>1646-9895</journal-id>
<journal-title><![CDATA[RISTI - Revista Ibérica de Sistemas e Tecnologias de Informação]]></journal-title>
<abbrev-journal-title><![CDATA[RISTI]]></abbrev-journal-title>
<issn>1646-9895</issn>
<publisher>
<publisher-name><![CDATA[AISTI - Associação Ibérica de Sistemas e Tecnologias de Informação]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1646-98952024000100087</article-id>
<article-id pub-id-type="doi">10.17013/risti.53.87-105</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelos de identificación de enfermedades cardiovasculares implementando técnicas de aprendizaje máquina: una revisión sistemática de la literatura]]></article-title>
<article-title xml:lang="en"><![CDATA[Models of identification cardiovascular diseases implementing machine learning techniques: a systematic literature review]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bovea]]></surname>
<given-names><![CDATA[Johan Mardini -]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Salcedo]]></surname>
<given-names><![CDATA[Dixon]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[De-la-Hoz-Franco]]></surname>
<given-names><![CDATA[Emiro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quiñonez]]></surname>
<given-names><![CDATA[Yadira]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jimenez-Roa]]></surname>
<given-names><![CDATA[Luz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoz-Avila]]></surname>
<given-names><![CDATA[Víctor De la]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sanchez]]></surname>
<given-names><![CDATA[Jhon González]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de la Costa-CUC Departamento de Ciencias de la Computación y Electrónica ]]></institution>
<addr-line><![CDATA[Barranquilla ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Sinaloa Facultad de informática de Mazatlán ]]></institution>
<addr-line><![CDATA[Mazatlán ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Centro de Enseñanza Colombo Alemán  ]]></institution>
<addr-line><![CDATA[Barranquilla ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>30</day>
<month>03</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>30</day>
<month>03</month>
<year>2024</year>
</pub-date>
<numero>53</numero>
<fpage>87</fpage>
<lpage>105</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952024000100087&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952024000100087&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952024000100087&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El uso de técnicas de Aprendizaje Automático (AA) en el área de la salud, específicamente en la identificación de enfermedades cardiovasculares (IEC), ha tenido un impacto significativo debido a la capacidad para analizar grandes cantidades de datos y extraer información relevante que puede ser esencial para la toma de decisiones médicas. Sin embargo, antes de ponerlos a disposición de los usuarios finales (médicos), se debe evaluar su capacidad para detectar sintomatologías relacionadas con enfermedades cardíacas utilizando puntos de referencia de conjuntos de datos en escenarios experimentales. Por lo tanto, es complicado determinar qué características utilizar en el proceso de evaluación y qué técnicas de ML son más adecuadas para la predicción de IEC. Este artículo presenta una revisión sistemática de la literatura sobre el procesamiento de conjuntos de datos basados en pruebas clínicas de enfermedades cardiovasculares y técnicas de AA. En este sentido, se realizó un análisis de las diferentes variables extraídas de publicaciones de revistas indexadas en bases de datos especializadas como Scopus, Web of Science, Science Direct, Biomed y Pubmed.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The use of Machine Learning (ML) techniques in the health area, specifically in the identification of cardiovascular diseases (IEC), has had a significant impact due to the ability to analyze large amounts of data and extract relevant information that can be essential for medical decision-making. However, before making them available to end users (doctors), their ability to detect heart disease-related symptomatology should be evaluated using benchmark data sets in experimental settings. Therefore, determining which features to use in the evaluation process and which ML techniques are most suitable for IEC prediction is complicated. This article presents a systematic literature review on processing cardiovascular disease clinical trial-based datasets and ML techniques. In this sense, the different variables were analyzed from journal publications indexed in specialized databases such as Scopus, Web of Science, Science Direct, Biomed, and Pubmed.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Enfermedades cardiovasculares]]></kwd>
<kwd lng="es"><![CDATA[técnicas de inteligencia artificial, conjunto de datos]]></kwd>
<kwd lng="es"><![CDATA[síndrome coronario agudo]]></kwd>
<kwd lng="en"><![CDATA[Cardiovascular diseases]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence techniques, data set]]></kwd>
<kwd lng="en"><![CDATA[acute coronary síndrome]]></kwd>
</kwd-group>
</article-meta>
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