<?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>2312-3893</journal-id>
<journal-title><![CDATA[Revista Virtual de la Sociedad Paraguaya de Medicina Interna]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. virtual Soc. Parag. Med. Int.]]></abbrev-journal-title>
<issn>2312-3893</issn>
<publisher>
<publisher-name><![CDATA[Sociedad Paraguaya de Medicina Interna]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2312-38932024000100010</article-id>
<article-id pub-id-type="doi">10.18004/rvspmi/2312-3893/2024.e11122412</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de algoritmos de aprendizaje supervisado para identificación de factores sociodemográficos asociados a síntomas depresivos en adultos peruanos: modelo CHAID, 2022]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of supervised learning algorithms to identify sociodemographic factors associated with depressive symptoms in Peruvian adults: CHAID model, 2022]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guevara Tirado]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Privada Norbert Wiener Facultad de Medicina Humana ]]></institution>
<addr-line><![CDATA[Lima Perú]]></addr-line>
<country>Peru</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>11</volume>
<numero>1</numero>
<fpage>10</fpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_arttext&amp;pid=S2312-38932024000100010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_abstract&amp;pid=S2312-38932024000100010&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.iics.una.py/scielo.php?script=sci_pdf&amp;pid=S2312-38932024000100010&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  la sintomatología depresiva es altamente prevalente en la población peruana. El uso del algoritmo de árbol de decisiones podría beneficiar en hallar grupos especialmente vulnerables a padecer síntomas depresivos.  Objetivo:  determinar los grupos especialmente vulnerables a tener síntomas depresivos según factores sociodemográficos mediante algoritmo de árbol de decisiones por aprendizaje automático.  Material y métodos:  se aplicó un diseño observacional, descriptivo, retrospectivo y transversal. Los datos provinieron de la encuesta nacional demográfica y de salud. La población fue 32.062 adultos. La variable dependiente fue: presencia de síntomas depresivos, y como variables explicativas: grupo etario, lengua materna, grupo étnico, nivel educativo, edad de inicio de consumo de alcohol, consumo de alcohol, estado conyugal, sexo. Se utilizó el algoritmo de árbol de decisiones mediante detección automática de interacciones mediante chi-cuadrado (CHAID).  Resultados:  las variables significativas en el algoritmo fueron: sexo, tipo de lengua materna, estado conyugal, grupo etario, nivel educativo alcanzado, clasificando de forma correcta 75,80% de los casos de síntomas depresivos. Los nodos asociados principalmente a la presencia de síntomas depresivos fueron: nodo 2 (sexo femenino), nodo 6 (adultos desde 39 años), nodo 13 (educación hasta secundaria). Según sexo, en mujeres, las variables principalmente asociadas fueron los correspondientes al nodo 2 (adultos desde los 39 años), nodo 5 (educación hasta secundaria) y nodo 13 (lengua materna originaria). En hombres, los nodos asociados principalmente a síntomas depresivos fueron el nodo 2 (lengua materna originaria), nodo 6 (adultos desde los 39 años) y nodo 11 (nivel educativo alcanzado hasta secundaria).  Conclusiones:  el principal grupo sociodemográfico asociado al desarrollo de síntomas depresivos son el sexo femenino, desde los 39 años y cuya educación ha llegado a la etapa escolar. El uso de algoritmos de aprendizaje automático es útil para crear herramientas de cribado de poblaciones vulnerables a padecer síntomas depresivos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Depressive symptoms are highly prevalent in the Peruvian population. The use of the decision tree algorithm could be beneficial in finding groups especially vulnerable to suffering from depressive symptoms.  Objective:  To determine the groups especially vulnerable to having depressive symptoms according to sociodemographic factors using a machine learning decision tree algorithm.  Material and methods:  An observational, descriptive, retrospective and cross-sectional design was applied. Data came from the National Demographic and Health Survey. The population was 32,062 adults and the dependent variable was the presence of depressive symptoms, and as explanatory variables: age group, mother tongue, ethnic group, educational level, age of onset of alcohol consumption, alcohol consumption, marital status, sex. The decision tree algorithm using automatic chi-square interaction detection (CHAID) was used.  Results:  The significant variables in the algorithm were sex, type of mother tongue, marital status, age group, educational level achieved, correctly classifying 75.80% of the cases of depressive symptoms. The nodes mainly associated with the presence of depressive symptoms were: node 2 (female sex), node 6 (adults from 39 years old), and node 13 (education up to secondary school). According to sex, in women, the variables mainly associated were those corresponding to node 2 (adults from 39 years of age), node 5 (education up to secondary school) and node 13 (original mother tongue). In men, the nodes mainly associated with depressive symptoms were node 2 (native mother tongue), node 6 (adults from 39 years of age) and node 11 (educational level reached up to secondary school).  Conclusions:  The main sociodemographic group associated with the development of depressive symptoms is the female sex, from the age of 39 and whose education has reached the school stage. The use of machine learning algorithms is useful to create screening tools for populations vulnerable to suffering from depressive symptoms.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[depresión]]></kwd>
<kwd lng="es"><![CDATA[epidemiología]]></kwd>
<kwd lng="es"><![CDATA[salud mental]]></kwd>
<kwd lng="es"><![CDATA[Perú]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[depression]]></kwd>
<kwd lng="en"><![CDATA[epidemiology]]></kwd>
<kwd lng="en"><![CDATA[mental health]]></kwd>
<kwd lng="en"><![CDATA[Peru]]></kwd>
</kwd-group>
</article-meta>
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