<?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>2665-2056</journal-id>
<journal-title><![CDATA[Revista de investigación e innovación en ciencias de la salud]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. Investig. Innov. Cienc. Salud]]></abbrev-journal-title>
<issn>2665-2056</issn>
<publisher>
<publisher-name><![CDATA[Fundación Universitaria María Cano]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2665-20562026000200005</article-id>
<article-id pub-id-type="doi">10.46634/riics.535</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aprendizaje de máquina explicativo para entender los determinantes sociales y clínicos de la diabetes de tipo 2 en México]]></article-title>
<article-title xml:lang="en"><![CDATA[Explainable Machine Learning to Understand the Social and Biological Determinants of Type 2 Diabetes in Mexico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cervantes-Guerrero]]></surname>
<given-names><![CDATA[Mario Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Carlos E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz]]></surname>
<given-names><![CDATA[Miguel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galván-Tejada]]></surname>
<given-names><![CDATA[Jorge I.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barros]]></surname>
<given-names><![CDATA[Rodrigo C.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Kupssinskü]]></surname>
<given-names><![CDATA[Lucas]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Zacatecas Unidad Académica de Ingeniería Eléctrica ]]></institution>
<addr-line><![CDATA[Zacatecas ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Mexicano del Seguro Social  ]]></institution>
<addr-line><![CDATA[Mexico City ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Pontifícia Universidade Católica do Rio Grande do Sul School of Technology Machine Learning Theory and Applications Lab]]></institution>
<addr-line><![CDATA[Porto Alegre ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2026</year>
</pub-date>
<volume>8</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S2665-20562026000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S2665-20562026000200005&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S2665-20562026000200005&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Introducción.  La diabetes mellitus tipo 2 (DM2) es una enfermedad multifactorial asociada no solo a factores clínicos, sino también a determinantes sociales como el ingreso, la educación y el acceso a los servicios de salud. Su prevalencia global continúa en aumento, especialmente en países de ingresos bajos y medios, lo que convierte su detección temprana y su manejo equitativo en una prioridad de salud pública.  Objetivo.  Evaluar e interpretar el desempeño de modelos de aprendizaje automático que integran datos clínicos y socioeconómicos para predecir la presencia de diabetes tipo 2 en una población mexicana.  Método.  Se analizó un conjunto de datos conformado por 898 pacientes con diabetes y 889 individuos no diabéticos seleccionados aleatoriamente del Hospital de Especialidades Siglo XXI (IMSS, Ciudad de México). Se entrenaron y validaron diez algoritmos supervisados (regresión logística, SVM, árbol de decisión, bosque aleatorio, KNN, naive Bayes, AdaBoost, LightGBM, CatBoost y XGBoost) mediante validación cruzada estratificada de 10 pliegues. El preprocesamiento incluyó imputación múltiple por ecuaciones encadenadas (MICE), normalización y codificación de variables categóricas. La interpretabilidad del modelo se analizó mediante SHapley Additive exPlanations (SHAP) para identificar los predictores más influyentes.  Resultados.  Los métodos de ensamble, particularmente XGBoost y LightGBM, mostraron el mejor desempeño (exactitud = 0.94, AUC &gt; 0.95). La glucosa, la presión arterial diastólica y la edad fueron los predictores más relevantes, mientras que las variables socioeconómicas como ingreso y educación aportaron valor predictivo complementario. La integración de variables contextuales mejoró la interpretabilidad sin comprometer el rendimiento predictivo.  Conclusiones.  Los modelos explicables de aprendizaje automático que integran información clínica y socioeconómica pueden mejorar la precisión diagnóstica y promover la equidad en la detección de la diabetes. Estas herramientas ofrecen un alto potencial para su aplicación en programas de salud pública, facilitando la identificación temprana de poblaciones en riesgo y fortaleciendo estrategias de prevención y atención más equitativas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Introduction.  Type 2 diabetes mellitus (T2DM) is a multifactorial disease associated not only with clinical factors but also with social determinants such as income, education, and access to healthcare. Its global prevalence continues to rise, particularly in low- and middle-income countries, making early detection and equitable management a public health priority.  Objective.  To evaluate and interpret the performance of machine learning models that incorporate both clinical and socioeconomic data to predict the presence of type 2 diabetes in a Mexican population.  Methods.  A dataset including 898 diabetic patients and 889 non-diabetic individuals randomly selected from the Hospital de Especialidades Siglo XXI (IMSS, Mexico City) was analyzed. Ten supervised algorithms (logistic regression, SVM, decision tree, random forest, KNN, naive bayes, AdaBoost, LightGBM, CatBoost, and XGBoost) were trained and validated using stratified 10-fold cross-validation. Data preprocessing included multiple imputation by chained equations (MICE), normalization, and encoding of categorical variables. Model interpretability was evaluated using SHapley Additive exPlanations (SHAP) to identify the most influential predictors.  Results.  Ensemble methods, particularly XGBoost and LightGBM, achieved the best performance (accuracy = 0.94, AUC &gt; 0.95). Glucose, diastolic blood pressure, and age were the strongest predictors, while socioeconomic variables such as income and education contributed additional predictive value. The integration of contextual variables improved model interpretability without reducing accuracy.  Conclusions.  Explainable machine learning models integrating both clinical and socioeconomic data can enhance diagnostic precision and promote equity in diabetes detection. These tools offer potential applications in public health programs, facilitating the early identification of at-risk populations and supporting more equitable prevention and care strategies.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="en"><![CDATA[explainable AI]]></kwd>
<kwd lng="en"><![CDATA[diabetes]]></kwd>
<kwd lng="en"><![CDATA[socioeconomic determinants]]></kwd>
<kwd lng="en"><![CDATA[SHAP]]></kwd>
<kwd lng="en"><![CDATA[Mexico]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial explicable]]></kwd>
<kwd lng="es"><![CDATA[diabetes]]></kwd>
<kwd lng="es"><![CDATA[determinantes socioeconómicos]]></kwd>
<kwd lng="es"><![CDATA[SHAP]]></kwd>
<kwd lng="es"><![CDATA[México.]]></kwd>
</kwd-group>
</article-meta>
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