<?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>2954-4122</journal-id>
<journal-title><![CDATA[Archivos de neurociencias (México)]]></journal-title>
<abbrev-journal-title><![CDATA[Arch. Neurocien. (Mex.)]]></abbrev-journal-title>
<issn>2954-4122</issn>
<publisher>
<publisher-name><![CDATA[Instituto Nacional de Neurología y Neurocirugía "Manuel Velasco Suárez"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2954-41222025000200057</article-id>
<article-id pub-id-type="doi">10.24875/anc.m24000028</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Esclerosis múltiple: caracterización del fenotipo progresivo y remitente-recurrente con aprendizaje automático]]></article-title>
<article-title xml:lang="en"><![CDATA[Multiple sclerosis: characterization of the progressive and relapsing-remitting phenotype with machine learning]]></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 Científica del Sur Facultad de Medicina Humana ]]></institution>
<addr-line><![CDATA[Lima ]]></addr-line>
<country>Perú</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2025</year>
</pub-date>
<volume>30</volume>
<numero>2</numero>
<fpage>57</fpage>
<lpage>64</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2954-41222025000200057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2954-41222025000200057&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2954-41222025000200057&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Antecedentes El uso de algoritmos de aprendizaje supervisado puede contribuir a construir modelos de clasificación y predicción eficientes en torno al fenotipo de esclerosis múltiple (EM).  Objetivo Identificar y caracterizar los factores asociados a los fenotipos de esclerosis múltiple primaria progresiva y remitente-recurrente utilizando un modelo de aprendizaje automático, basado en árboles de decisión.  Método Estudio analítico y transversal de una fuente secundaria, las variables fueron fenotipo, edad, sexo, glucocorticoides, consumo de cigarro, terapia modificadora. Se utilizó el árbol de decisiones mediante detector de interacciones automáticas de chi-cuadrado y regresión logística binaria.  Resultados El árbol clasificó correctamente (87%) como características asociadas a EM primaria progresiva (EMPP) a pacientes con antecedentes tabáquicos entre 51 a 70 años. En EM remitente recurrente (EMRR), el grupo con mayor asociación fue el de mujeres entre 18 a 50 años. Al incluir medicamentos modificadores de la enfermedad (pronósticos correctos: 89.70%), los grupos asociados a EMPP fueron antecedentes de fumar, tratados con teriflunomida, rituximab, glatiramero y ocrelizumab de entre 51 a 70 años, hombres entre 18 a 50 años con ocrelizumab y rituximab. Para EMRR, fueron mujeres de 18 a 50 años con ocrelizumab, rituximab. Pacientes de 18 a 50 años con dimetilfumarato, teriflunomida, interferón, glatiramero, fingolimod, natalizumab, cladribina y alemtuzumab.  Conclusiones El aprendizaje automático mediante arboles de decisión con datos de fácil acceso es eficiente en la clasificación rápida de factores personales y perfiles farmacológicos asociados a EMRR y EMPP. Asimismo, el antecedente tabáquico es un predictor de EMPP. El árbol de decisión podría ayudar a neurólogos y epidemiólogos proporcionando información adicional para tomar decisiones clínicas, terapéuticas y epidemiológicas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Background Supervised learning algorithms can contribute to building efficient classification and prediction models around the multiple sclerosis (MS) phenotype.  Objective To identify and characterize the factors associated with primary progressive and relapsing-remitting multiple sclerosis phenotypes using a machine learning model, based on decision trees.  Method This was an analytical and cross-sectional study from a secondary source. The variables were phenotype, age, sex, glucocorticoids, cigarette consumption, and modifying therapy. The decision tree was used using the chi-square automatic interaction detector and binary logistic regression.  Results The tree correctly classified (87%) patients with a smoking history between 51 and 70 years of age as characteristics associated with primary progressive MS (PPMS). In relapsing remitting MS (RRMS), the group with the greatest association was women between 18 and 50 years old. When including disease-modifying medications (correct prognoses: 89.70%), the groups associated with PPMS were history of smoking, treated with teriflunomide, rituximab, glatiramer and ocrelizumab between 51 and 70 years old, men between 18 and 50 years old with ocrelizumab and rituximab. For RRMS, they were women 18 to 50 years old with ocrelizumab and rituximab. Patients aged 18 to 50 years with dimethyl fumarate, teriflunomide, interferon, glatiramer, fingolimod, natalizumab, cladribine, and alemtuzumab.  Conclusions Machine learning using decision trees with easily accessible data is efficient in rapidly classifying personal factors and pharmacological profiles associated with RRMS and PPMS. Likewise, smoking history is a predictor of PPMS. The decision tree could help neurologists and epidemiologists by providing additional information to make clinical, therapeutic, and epidemiological decisions.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Esclerosis múltiple recurrente-remitente]]></kwd>
<kwd lng="es"><![CDATA[Esclerosis múltiple crónica progresiva]]></kwd>
<kwd lng="es"><![CDATA[Características de la población]]></kwd>
<kwd lng="es"><![CDATA[Aprendizaje automático supervisado]]></kwd>
<kwd lng="es"><![CDATA[Toma de decisiones asistida por computador]]></kwd>
<kwd lng="en"><![CDATA[Relapsing-remitting multiple sclerosis]]></kwd>
<kwd lng="en"><![CDATA[Chronic progressive multiple sclerosis]]></kwd>
<kwd lng="en"><![CDATA[Population characteristics]]></kwd>
<kwd lng="en"><![CDATA[Supervised machine learning]]></kwd>
<kwd lng="en"><![CDATA[Computer-assisted decision making]]></kwd>
</kwd-group>
</article-meta>
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