Data Mining and Endocrine Diseases: A New Way to Classify?
Fecha
2018-04
2018-04
Autor
Salazar, Juan
Espinoza, Cristobal
Mindiola, Andres
Bermudez, Valmore
Metadatos
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Resumen
Data 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.
Enlace para referencia:
http://hdl.handle.net/20.500.12442/2303
http://hdl.handle.net/20.500.12442/2303
Enlace al recurso externo:
https://doi.org/10.1016/j.arcmed.2018.08.005
https://doi.org/10.1016/j.arcmed.2018.08.005