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dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionales
dc.contributor.authorSalazar, Juan
dc.contributor.authorEspinoza, Cristobal
dc.contributor.authorMindiola, Andres
dc.contributor.authorBermudez, Valmore
dc.date.accessioned2018-10-03T19:39:20Z
dc.date.available2018-10-03T19:39:20Z
dc.date.issued2018-04
dc.identifier.issn01884409
dc.identifier.urihttp://hdl.handle.net/20.500.12442/2303
dc.description.abstractData mining consists of using large database analysis to detect patterns, relationships and models in order to describe (or even predict) the appearance of a future event; to accomplish this, it uses classification methods, rules of association, regression patterns, link and cluster analyses. Recently this approach has been used to propose a new diabetes mellitus classification, using information analysis techniques through which the selection bias minimally influences categorization, this new focus that includes data mining previously implemented to predict, identify biomarkers, complications, therapies, health policies, genetic and environmental effects of this disease; it could be generalized in the field of endocrinology, in the classification of other endocrine diseases.en
dc.language.isoenen
dc.publisherElsevieres
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceArchives of Medical Researchen
dc.sourceVol. 49, No. 3 (2018)es
dc.source.urihttps://doi.org/10.1016/j.arcmed.2018.08.005en
dc.subjectData miningen
dc.subjectClassificationen
dc.subjectEndocrine diseaseen
dc.subjectDiabetes mellitusen
dc.subjectInformation analysisen
dc.titleData Mining and Endocrine Diseases: A New Way to Classify?en
dc.typeArticleen
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