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Statistics applied in the classification and prediction of depression: A systematic review [Machine learning aplicado en la clasificación y predicción de la depresión: Una revisión sistemática]

dc.contributor.authorCastrillon S.O
dc.contributor.authorMarín L.M.G
dc.contributor.authorVillegas H.H.J
dc.contributor.authorEscobar C.C.P.
dc.date.accessioned2022-09-14T14:34:09Z
dc.date.available2022-09-14T14:34:09Z
dc.date.created2022
dc.identifier.issn16469895
dc.identifier.urihttp://hdl.handle.net/11407/7580
dc.descriptionThe main challenge for health insurers is to properly manage the disease as well as the health of its members, emphasizing the prevention and implementation of actions that allow the anticipation and prediction of the disease. This article presents a systematic review of the literature on the main machine learning methodologies that allow, through the prediction of mental illnesses, to carry out an early intervention. It was found that the main methodologies for this purpose are statistical models such as logistic regression, Vector support machine and random forest; and that the different indicators of neuroimaging use of cell phonesbecome fundamental predictor variables when it comes to predicting mental illnesses. © 2022, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.eng
dc.language.isospa
dc.publisherAssociacao Iberica de Sistemas e Tecnologias de Informacao
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85127594818&partnerID=40&md5=a8b4f64be78833e1ff7345a0c34367e3
dc.sourceRISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
dc.titleStatistics applied in the classification and prediction of depression: A systematic review [Machine learning aplicado en la clasificación y predicción de la depresión: Una revisión sistemática]
dc.typeReview
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Sistemas
dc.publisher.programCiencias Básicas
dc.type.spaRevisión
dc.subject.keywordDepressioneng
dc.subject.keywordMachine learningeng
dc.subject.keywordSystematic revieweng
dc.relation.citationissueSpecial Issue 47
dc.relation.citationstartpage363
dc.relation.citationendpage375
dc.publisher.facultyFacultad de Ingenierías
dc.publisher.facultyFacultad de Ciencias Básicas
dc.affiliationCastrillon, S.O., Maestría en modelación y ciencia computacional, Universidad de Medellín, Sabaneta, 055450, Colombia
dc.affiliationMarín, L.M.G., Profesora titular Facultad de ingenierías, carrera 74 # 41 – 16, Medellín, 050032, Colombia
dc.affiliationVillegas, H.H.J., Universidad de Medellín, grupo Modelación y Computación Científica, Medellín, 050026, Colombia
dc.affiliationEscobar, C.C.P., Universidad de Medellín, grupo Modelación y Computación Científica, Medellín, 050026, Colombia
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dc.type.coarhttp://purl.org/coar/resource_type/c_dcae04bc
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.type.driverinfo:eu-repo/semantics/review
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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