<?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>1659-0201</journal-id>
<journal-title><![CDATA[Población y Salud en Mesoamérica]]></journal-title>
<abbrev-journal-title><![CDATA[PSM]]></abbrev-journal-title>
<issn>1659-0201</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Costa Rica. Centro Centroamericano de Población]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1659-02012022000200001</article-id>
<article-id pub-id-type="doi">10.15517/psm.v20i1.48217</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[An empirical analysis of homicides in Mexico through Machine Learning and statistical design of experiments]]></article-title>
<article-title xml:lang="es"><![CDATA[Análisis empírico de homicidios en México por medio de Machine Learning y diseño estadístico de experimentos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silva Urrutia]]></surname>
<given-names><![CDATA[Jose Eliud]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villalobos]]></surname>
<given-names><![CDATA[Miguel A.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Anáhuac México Facultad de Ciencias Actuariales ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Anáhuac México Facultad de Ciencias Actuariales ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2022</year>
</pub-date>
<volume>20</volume>
<numero>1</numero>
<fpage>1</fpage>
<lpage>27</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1659-02012022000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1659-02012022000200001&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1659-02012022000200001&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Homicide is one of the most important mortality causes that has reduced the Mexican life expectancy. That is why the aim of this work is to identify some sociodemographic and economic factors that can help explain homicides in Mexico and measure their impact, assuming the current conditions prevail. To do that, several Machine Learning (ML) methods were evaluated. The C5.0 model is best suited for the data at hand. After fine-tuning the algorithm, we used the estimated model to identify the main factors that explain homicides. Among these factors, eleven were selected that can be influenced by direct changes in domestic public policy, laws and/or regulations. These were used as input in a two-level fractional factorial Statistical Design of Experiments (DOE) to estimate their main effects and possible interactions. Although several of these factors had statistically significant effects on homicide rate, the one that had the biggest and direct impact from a practical perspective, was the Rule of Law Index (RLI). In fact, if we assumed that all states had the median RLI of 0.37, implementing domestic policies and procedures to move them all to the best RLI level could significantly reduce homicide rates.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El homicidio es una de las principales causas de muerte que ha reducido la esperanza de vida de los mexicanos. El objetivo de este trabajo es identificar algunos factores sociodemográficos y económicos que puedan ayudar a explicar homicidios en México y medir su impacto, suponiendo que las condiciones actuales permanecen. Para lograrlo, comparamos diferentes métodos de Aprendizaje de Máquina (AM). Para tal fin, se encuentra que el modelo C5.0 es el más adecuado. Después de hacer una calibración final del modelo, lo utilizamos para determinar los veinticinco principales factores que explican el fenómeno de homicidios. Se seleccionan 11 factores que se consideran pueden ser influenciados directamente por cambios en políticas públicas, leyes y/o regulaciones. Estos predictores fueron utilizados como entrada en un diseño de experimentos factorial fraccionado con dos niveles para estimar los principales efectos principales e interacciones posibles. A pesar de que varios de estos factores tuvieron impactos estadísticamente significativos, el que mostró tener el mayor impacto directo desde una perspectiva práctica fue el Índice de Estado de Derecho (IED). De hecho, asumiendo que todos los estados tuvieran el valor de IED de 0.37, correspondiente a la mediana en todo el país, si se implementaran políticas y procedimientos para ubicar a todos los estados al nivel del mejor estado en términos de IED, se lograría una reducción altamente significativa en la incidencia de homicidios en México.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[mortality]]></kwd>
<kwd lng="en"><![CDATA[homicides]]></kwd>
<kwd lng="en"><![CDATA[C5.0]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[Statistical Design of Experiments]]></kwd>
<kwd lng="es"><![CDATA[mortalidad]]></kwd>
<kwd lng="es"><![CDATA[homicidios]]></kwd>
<kwd lng="es"><![CDATA[C5.0]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje de Máquina]]></kwd>
<kwd lng="es"><![CDATA[Diseño Estadístico de Experimentos]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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