<?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>S1646-98952025000100003</article-id>
<article-id pub-id-type="doi">10.17013/risti.n.57.3-18</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo para identificar hogares beneficiarios de programas de transferencias monetarias: una comparación de técnicas de machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Predictive model for beneficiary households in cash transfer programs: a comparison of machine learning techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lopez]]></surname>
<given-names><![CDATA[Jiang Wagner Mamani]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Lopez]]></surname>
<given-names><![CDATA[Juliana Mery Bautista]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aguaded]]></surname>
<given-names><![CDATA[Ignacio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional de San Agustín de Arequipa  ]]></institution>
<addr-line><![CDATA[Arequipa ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Huelva  ]]></institution>
<addr-line><![CDATA[Huelva ]]></addr-line>
<country>Spain</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2025</year>
</pub-date>
<numero>57</numero>
<fpage>3</fpage>
<lpage>18</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S1646-98952025000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S1646-98952025000100003&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S1646-98952025000100003&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Los programas de transferencias monetarias son una herramienta clave para reducir la pobreza y mejorar el bienestar de los hogares vulnerables en países en desarrollo. Sin embargo, la correcta selección de beneficiarios sigue siendo un desafío. Este estudio evalúa distintas técnicas de machine learning para predecir la participación del programa Juntos en Perú, empleando datos de la Encuesta Nacional de Hogares (ENAHO) del 2023. Se compararon modelos como regresión logística, árboles de decisión, máquina de soporte vectorial, gradient boosting machine, bosque aleatorio, LightGBM, XGBoost y CatBoost. Los resultados muestran que XGBoost presenta el mejor desempeño en la clasificación de beneficiarios. Estos hallazgos resaltan el potencial de las técnicas de machine learning para fortalecer la asignación de recursos en programas sociales. Su implementación impulsaría la modernización de la gestión pública, permitiendo una gestión de recursos económicos fundamentada en datos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Cash transfer programs are a key tool for reducing poverty and improving the well-being of vulnerable households in developing countries. However, the accurate selection of beneficiaries remains a challenge. This study evaluates different machine learning techniques to predict participation in the Juntos program in Peru, using data from the 2023 National Household Survey (ENAHO). Models such as logistic regression, decision trees, support vector machine, gradient boosting machine, random forest, LightGBM, XGBoost, and CatBoost were compared. The results show that XGBoost achieves the best performance in beneficiary classification. These findings highlight the potential of machine learning techniques to enhance the allocation of resources in social programs. Their implementation would drive the modernization of public management, enabling data-driven economic resource management.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Transferencias Monetarias]]></kwd>
<kwd lng="es"><![CDATA[Machine Learning]]></kwd>
<kwd lng="es"><![CDATA[XGBoost]]></kwd>
<kwd lng="es"><![CDATA[Optimización Bayesiana]]></kwd>
<kwd lng="en"><![CDATA[Cash Transfers]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[XGBoost]]></kwd>
<kwd lng="en"><![CDATA[Bayesian Optimization]]></kwd>
</kwd-group>
</article-meta>
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