Mostrar el registro sencillo del ítem

dc.rights.licenseLicencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalspa
dc.contributor.authorSalazar, Juan
dc.contributor.authorEspinoza, Cristobal
dc.contributor.authorMindiola, Andres
dc.contributor.authorBermudez, Valmore
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.eng
dc.sourceArchives of Medical Researcheng
dc.sourceVol. 49, No. 3 (2018)spa
dc.subjectData miningeng
dc.subjectEndocrine diseaseeng
dc.subjectDiabetes mellituseng
dc.subjectInformation analysiseng
dc.titleData Mining and Endocrine Diseases: A New Way to Classify?eng
dcterms.referencesR. Kumar, B.T. Shaikh, A.K. Chandio, et al. Role of Health Management Information System in disease reporting at a rural district of Sindh Pak J Public Health, 2 (2012), pp. 10-12eng
dcterms.referencesI. ŢĂranu Data mining in healthcare: decision making and precision Database Systems Journal, VI (2015), pp. 33-40eng
dcterms.referencesJ. Semerdjian, S. Frank An Ensemble Classifier for Predicting the Onset of Type II Diabetes arXiv:1708.07480. Available:, Accessed 15th Jun 2018eng
dcterms.referencesA. Agarwal, C. Baechle, R.S. Behara, V. Rao Multi-method approach to wellness predictive modeling J Big Data, 3 (2016), p. 15eng
dcterms.referencesH.P. Himsworth The syndrome of diabetes mellitus and its causes Lancet, 1 (1949), pp. 465-473eng
dcterms.referencesE. Ahlqvist, P. Storm, A. Käräjämäki, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables Lancet Diabetes Endocrinol, 6 (2018), pp. 361-369eng
dcterms.referencesI. Kavakiotis, O. Tsave, A. Salifoglou, et al. Machine Learning and Data Mining Methods in Diabetes Research Comput Struct Biotechnol J, 15 (2017), pp. 104-116eng
dcterms.referencesV. Bermúdez, J. Rojas, J. Salazar, et al. Sensitivity and Specificity Improvement in Abdominal Obesity Diagnosis Using Cluster Analysis during Waist Circumference Cut-Off Point Selectioneng
dcterms.referencesM. Jajroudi, T. Baniasadi, L. Kamkar, F. Arbabi, M. Sanei, M. Ahmadzade Prediction of survival in thyroid cancer using data mining technique Technol Cancer Res Treat, 13 (2014), pp. 353-359eng
dcterms.referencesD. Dewailly, M.Š. Alebić, A. Duhamel, N. Stojanović Using cluster analysis to identify a homogeneous subpopulation of women with polycystic ovarian morphology in a population of non-hyperandrogenic women with regular menstrual cycles Hum Reprod, 29 (2014), pp. 2536-2543eng

Ficheros en el ítem


No hay ficheros asociados a este ítem.

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos
    Artículos científicos evaluados por pares

Mostrar el registro sencillo del ítem