<?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>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532023000200036</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v90n226.105616</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Classification of COVID-19 associated symptomatology using machine learning]]></article-title>
<article-title xml:lang="es"><![CDATA[Clasificación de la sintomatología asociada a la COVID-19 mediante aprendizaje automático]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ramirez-Bautista]]></surname>
<given-names><![CDATA[Julian Andres]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Chaparro-Cárdenas]]></surname>
<given-names><![CDATA[Silvia L.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gamboa-Contreras]]></surname>
<given-names><![CDATA[Wilson]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerrero-Salazar]]></surname>
<given-names><![CDATA[William]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Huerta-Ruelas]]></surname>
<given-names><![CDATA[Jorge Adalberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Fundación Universitaria de San Gil-Unisangil Departamento de Investigación ]]></institution>
<addr-line><![CDATA[San Gil ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada-Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[Querétaro ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2023</year>
</pub-date>
<volume>90</volume>
<numero>226</numero>
<fpage>36</fpage>
<lpage>43</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532023000200036&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532023000200036&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532023000200036&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The health situation caused by the SARS-Cov2 coronavirus, posed major challenges for the scientific community. Advances in artificial intelligence are a very useful resource, but it is important to determine which symptoms presented by positive cases of infection are the best predictors. A machine learning approach was used with data from 5,434 people, with eleven symptoms: breathing problems, dry cough, sore throat, running nose, history of asthma, chronic lung, headache, heart disease, hypertension, diabetes, and fever. Based on public data from Kaggle with WHO standardized symptoms. A model was developed to detect COVID-19 positive cases using a simple machine learning model. The results of 4 loss functions and by SHAP values, were compared. The best loss function was Binary Cross Entropy, with a single hidden layer configuration with 10 neurons, achieving an F1 score of 0.98 and the model was rated with an area under the curve of 0.99 aucROC.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La situación sanitaria provocada por el coronavirus SARS-Cov2 plantea grandes retos a la comunidad científica. Los avances en inteligencia artificial son un recurso muy útil, pero es importante determinar qué síntomas presentados por los casos positivos de infección son los mejores predictores. Se utilizó un enfoque de aprendizaje automático con datos de 5.434 personas, con once síntomas: problemas respiratorios, tos seca, dolor de garganta, secreción nasal, antecedentes de asma, pulmón crónico, dolor de cabeza, enfermedad cardíaca, hipertensión, diabetes y fiebre. Basado en datos públicos de Kaggle con síntomas estandarizados por la OMS. Se desarrolló un modelo para detectar los casos positivos de COVID-19 utilizando un modelo simple de aprendizaje automático. Se compararon los resultados de 4 funciones de pérdida y por valores SHAP. La mejor función de pérdida fue la Entropía Cruzada Binaria, con una configuración de una sola capa oculta con 10 neuronas, logrando una puntuación F1 de 0,98 y el modelo fue calificado con un área bajo la curva de 0,99 aucROC.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[computer-aided diagnosis: COVID-19]]></kwd>
<kwd lng="en"><![CDATA[disease diagnosis]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico asistido por ordenador]]></kwd>
<kwd lng="es"><![CDATA[COVID-19]]></kwd>
<kwd lng="es"><![CDATA[diagnóstico de enfermedades]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Peña-Reyes]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Sipper]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Evolutionary Computation in medicine: an overview,]]></article-title>
<source><![CDATA[Artif. Intell. Med]]></source>
<year>2000</year>
<volume>19</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-23</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tan]]></surname>
<given-names><![CDATA[K.C.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[Q.C.]]></given-names>
</name>
<name>
<surname><![CDATA[Heng]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[T.H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Evolutionary computing for knowledge discovery in medical diagnosis]]></article-title>
<source><![CDATA[Artif. Intell. Med]]></source>
<year>2003</year>
<volume>27</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>129-54</page-range></nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Liu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[An automatic recognition system for patients with movement disorders based on wearable sensors]]></source>
<year>2014</year>
<conf-name><![CDATA[ 9thIEEE Conf. Ind. Electron]]></conf-name>
<conf-date>2014</conf-date>
<conf-loc> </conf-loc>
<page-range>1948-53</page-range></nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Andrikopoulou]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Symptoms and critical illness among obstetric patients with coronavirus disease 2019 (COVID-19) infection]]></article-title>
<source><![CDATA[Obstet. Gynecol]]></source>
<year>2020</year>
<volume>136</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>291-9</page-range></nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Amenta]]></surname>
<given-names><![CDATA[E.M.]]></given-names>
</name>
<name>
<surname><![CDATA[Spallone]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Rodriguez-Barradas]]></surname>
<given-names><![CDATA[M.C.]]></given-names>
</name>
<name>
<surname><![CDATA[El--Sahly]]></surname>
<given-names><![CDATA[H.M.]]></given-names>
</name>
<name>
<surname><![CDATA[Atmar]]></surname>
<given-names><![CDATA[R.L.]]></given-names>
</name>
<name>
<surname><![CDATA[Kulkarni]]></surname>
<given-names><![CDATA[P.A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Postacute COVID-19: an overview and approach to classification]]></article-title>
<source><![CDATA[Open Forum Infect. Dis.]]></source>
<year>2020</year>
<volume>7</volume>
<numero>12</numero>
<issue>12</issue>
<page-range>1-7</page-range></nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Maghdid]]></surname>
<given-names><![CDATA[H.S.]]></given-names>
</name>
<name>
<surname><![CDATA[Ghafoor]]></surname>
<given-names><![CDATA[K.Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Sadiq]]></surname>
<given-names><![CDATA[A.S.]]></given-names>
</name>
<name>
<surname><![CDATA[Curran]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Rawat]]></surname>
<given-names><![CDATA[D.B.]]></given-names>
</name>
<name>
<surname><![CDATA[Rabie]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A novel AI-enabled framework to diagnose coronavirus COVID-19 using smartphone embedded sensors: design study]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2020</year>
<page-range>1-7</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alimadadi]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Aryal]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Manandhar]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Munroe]]></surname>
<given-names><![CDATA[P.B.]]></given-names>
</name>
<name>
<surname><![CDATA[Joe]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Cheng]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Artificial intelligence and machine learning to fight Covid-19, Physiol]]></article-title>
<source><![CDATA[Genomics]]></source>
<year>2020</year>
<volume>52</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>200-2</page-range></nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zoabi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shomron]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<source><![CDATA[COVID-19 diagnosis prediction by symptoms of tested individuals : a machine learning approach]]></source>
<year>2020</year>
<publisher-name><![CDATA[NPJ Digital Medicine]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alafif]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Bajaba]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[Machine and deep learning towards COVID-19 diagnosis and treatment: survey]]></source>
<year>2020</year>
<publisher-name><![CDATA[Challenges]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zoabi]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Deri-Rozov]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Shomron]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning-based prediction of COVID-19 diagnosis based on symptoms]]></article-title>
<source><![CDATA[npj Digit. Med]]></source>
<year>2021</year>
<volume>4</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[An interpretable machine learning framework for accurate severe vs non-severe COVID-19 clinical type classification]]></source>
<year>2020</year>
<publisher-name><![CDATA[medRxiv]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ahamad]]></surname>
<given-names><![CDATA[M.M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A machine learning model to identify early stage symptoms of SARS-Cov-2 infected patients]]></article-title>
<source><![CDATA[Expert Syst. Appl.]]></source>
<year>2020</year>
<volume>160</volume>
</nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Khanday]]></surname>
<given-names><![CDATA[A.M.U.D.]]></given-names>
</name>
<name>
<surname><![CDATA[Rabani]]></surname>
<given-names><![CDATA[S.T.]]></given-names>
</name>
<name>
<surname><![CDATA[Khan]]></surname>
<given-names><![CDATA[Q.R.]]></given-names>
</name>
<name>
<surname><![CDATA[Rouf]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Mohi Ud Din]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine learning based approaches for detecting COVID-19 using clinical text data]]></article-title>
<source><![CDATA[Int. J. Inf. Technol]]></source>
<year>2020</year>
<volume>12</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>731-9</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Smarr]]></surname>
<given-names><![CDATA[B.L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feasibility of continuous fever monitoring using wearable devices]]></article-title>
<source><![CDATA[Sci. Rep.]]></source>
<year>2020</year>
<volume>10</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Usha-Ruby]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Theerthagiri]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Jeena-Jacob]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Vamsidhar]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Binary cross entropy with deep learning technique for image classification]]></article-title>
<source><![CDATA[Int. J. Adv. Trends Comput. Sci. Eng]]></source>
<year>2020</year>
<volume>9</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>5393-7</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Valencia]]></surname>
<given-names><![CDATA[A.M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Construcción de la distribución de pérdidas y el problema de agregación de riesgo operativo bajo modelos LDA: una revisión]]></article-title>
<source><![CDATA[Revista Ingenierías Universidad de Medellín]]></source>
<year>2013</year>
<volume>12</volume>
<numero>23</numero>
<issue>23</issue>
<page-range>71-82</page-range></nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Bovik]]></surname>
<given-names><![CDATA[A.C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Mean squared error: Love it or leave it?. A new look at signal fidelity measures]]></article-title>
<source><![CDATA[IEEE Signal Process. Mag]]></source>
<year>2009</year>
<volume>6</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>98-117</page-range></nlm-citation>
</ref>
<ref id="B18">
<label>[18]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Meyer]]></surname>
<given-names><![CDATA[G.P.]]></given-names>
</name>
</person-group>
<source><![CDATA[An alternative probabilistic interpretation of the huber loss]]></source>
<year>2019</year>
<page-range>5261-9</page-range><publisher-name><![CDATA[arXiv]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>[19]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lundberg]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[S.-I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Unified approach to interpreting model predictions]]></article-title>
<source><![CDATA[Adv. Neural Inf. Process. Syst]]></source>
<year>2017</year>
<volume>2017</volume>
<page-range>4766-75</page-range></nlm-citation>
</ref>
<ref id="B20">
<label>[20]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mangalathu]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Hwang]]></surname>
<given-names><![CDATA[S.H.]]></given-names>
</name>
<name>
<surname><![CDATA[Jeo]]></surname>
<given-names><![CDATA[J.S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach]]></article-title>
<source><![CDATA[Eng. Struct]]></source>
<year>2020</year>
<volume>219</volume>
<page-range>110927</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>[21]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[&#352;trumbelj]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Kononenko]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Explaining prediction models and individual predictions with feature contributions]]></article-title>
<source><![CDATA[Knowl. Inf. Syst.]]></source>
<year>2014</year>
<volume>41</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>647-65</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
