<?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>1870-7203</journal-id>
<journal-title><![CDATA[Acta médica Grupo Ángeles]]></journal-title>
<abbrev-journal-title><![CDATA[Acta méd. Grupo Ángeles]]></abbrev-journal-title>
<issn>1870-7203</issn>
<publisher>
<publisher-name><![CDATA[Grupo Ángeles, Servicios de Salud]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1870-72032025000400323</article-id>
<article-id pub-id-type="doi">10.35366/120510</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Utilidad clínica del Machine Learning con Python para la predicción de factores de riesgo cardiovascular]]></article-title>
<article-title xml:lang="en"><![CDATA[Clinical utility of Machine Learning with Python for predicting cardiovascular risk factors]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Delgado Ayala]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Díaz Greene]]></surname>
<given-names><![CDATA[Enrique Juan]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rodríguez Weber]]></surname>
<given-names><![CDATA[Federico Leopoldo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad La Salle México Facultad Mexicana de Medicina ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad La Salle México Facultad Mexicana de Medicina ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad La Salle México Facultad Mexicana de Medicina ]]></institution>
<addr-line><![CDATA[Ciudad de México ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2025</year>
</pub-date>
<volume>23</volume>
<numero>4</numero>
<fpage>323</fpage>
<lpage>328</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1870-72032025000400323&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1870-72032025000400323&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1870-72032025000400323&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen:  Introducción:  Demostramos la facilidad y el potencial del Machine Learning para mejorar la detección y prevención de factores de riesgo cardiovascular; además, enfatizamos el análisis de variables numéricas continuas, frecuentemente omitidas en investigaciones, subrayando la innovación del enfoque de nuestro estudio.  Objetivo:  Evaluar con Python mediante métricas de rendimiento, la aplicabilidad de métodos predictivos (árbol de decisión, bosque aleatorio y K-Nearest Neighbors [KNN]).  Material y métodos:  Realizamos una búsqueda en PubMed, Google Scholar, cubriendo el periodo de 1995 a 2023, los términos incluyeron &#8220;aprendizaje automático&#8221; y &#8220;machine learning prediction model&#8221;. La bibliografía adicional se identificó mediante búsquedas complementarias en internet y medios físicos. Se incluyeron datos somatométricos y de laboratorio de una población de trabajadores de un hospital en México.  Resultados y conclusión:  El bosque aleatorio mostró el mejor desempeño tanto en variables numéricas como categóricas. Las numéricas se evaluaron con raíz del error cuadrático medio (RMSE), error absoluto medio (MAE), error cuadrático medio (MSE) y coeficiente de determinación (R2), mientras que las variables categóricas con exactitud, precisión, sensibilidad, puntaje F1 y ROC/AUC. Esto demuestra la robustez del bosque aleatorio en el manejo de diferentes tipos de datos, sugiriendo un gran potencial en contextos clínicos para la detección temprana de riesgos y estrategias de intervención.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract:  Introduction:  We demonstrate the ease and potential of machine learning in enhancing the detection and prevention of cardiovascular risk factors. Our study emphasizes the analysis of continuous numerical variables, often overlooked in research, highlighting the innovative approach of our investigation.  Objective:  To assess, using Python and performance metrics, the applicability of predictive methods (decision tree, random forest, and K-Nearest Neighbors [KNN]).  Material and methods:  A search was conducted on PubMed and Google Scholar, covering 1995 to 2023. Terms included &#8220;aprendizaje automático&#8221; and &#8220;machine learning prediction model&#8221;. Additional literature was sourced through supplementary online searches and physical media. The study includes somatometric and laboratory data from a population of hospital workers in Mexico.  Results and conclusion:  The Random Forest model exhibited superior performance for numerical and categorical variables. Numerical variables were evaluated using root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), and coefficient of determination (R2). In contrast, categorical variables were assessed with accuracy, precision, sensitivity, F1-score, and ROC/AUC. This proves the Random Forest&#8217;s robustness in handling diverse data types, suggesting its significant potential for early risk detection and intervention strategies in clinical settings.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[factores de riesgo cardiovascular]]></kwd>
<kwd lng="es"><![CDATA[predicción clínica]]></kwd>
<kwd lng="es"><![CDATA[modelos predictivos]]></kwd>
<kwd lng="es"><![CDATA[Python]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[cardiovascular risk factors]]></kwd>
<kwd lng="en"><![CDATA[clinical prediction]]></kwd>
<kwd lng="en"><![CDATA[predictive models]]></kwd>
<kwd lng="en"><![CDATA[Python]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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