Data Mining and Endocrine Diseases: A New Way to Classify?

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. Recent...

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Autores:
Salazar, Juan
Espinoza, Cristobal
Mindiola, Andres
Bermudez, Valmore
Tipo de recurso:
Fecha de publicación:
2018
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/2303
Acceso en línea:
http://hdl.handle.net/20.500.12442/2303
Palabra clave:
Data mining
Classification
Endocrine disease
Diabetes mellitus
Information analysis
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
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spelling Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Salazar, Juanfbd053e7-5aea-424c-812f-92153ecb9181-1Espinoza, Cristobal9431d6c1-8553-4395-9301-476c5f3ebc9a-1Mindiola, Andres0205aea7-c0e9-4303-b053-6cc043914928-1Bermudez, Valmore6bcbb12f-5c0b-46ea-9a9a-e059644f7f4e-12018-10-03T19:39:20Z2018-10-03T19:39:20Z2018-0401884409http://hdl.handle.net/20.500.12442/2303Data 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.engElsevierArchives of Medical ResearchVol. 49, No. 3 (2018)https://doi.org/10.1016/j.arcmed.2018.08.005Data miningClassificationEndocrine diseaseDiabetes mellitusInformation analysisData Mining and Endocrine Diseases: A New Way to Classify?articlehttp://purl.org/coar/resource_type/c_6501R. 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. 10-12I. ŢĂranu Data mining in healthcare: decision making and precision Database Systems Journal, VI (2015), pp. 33-40J. Semerdjian, S. Frank An Ensemble Classifier for Predicting the Onset of Type II Diabetes arXiv:1708.07480. Available: https://arxiv.org/pdf/1708.07480.pdf, Accessed 15th Jun 2018A. Agarwal, C. Baechle, R.S. Behara, V. Rao Multi-method approach to wellness predictive modeling J Big Data, 3 (2016), p. 15H.P. Himsworth The syndrome of diabetes mellitus and its causes Lancet, 1 (1949), pp. 465-473E. 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. 361-369I. Kavakiotis, O. Tsave, A. Salifoglou, et al. Machine Learning and Data Mining Methods in Diabetes Research Comput Struct Biotechnol J, 15 (2017), pp. 104-116V. Bermúdez, J. Rojas, J. Salazar, et al. Sensitivity and Specificity Improvement in Abdominal Obesity Diagnosis Using Cluster Analysis during Waist Circumference Cut-Off Point SelectionM. 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. 353-359D. 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. 2536-2543LICENSElicense.txtlicense.txttext/plain; charset=utf-8368https://bonga.unisimon.edu.co/bitstreams/03b2241f-6a1d-416e-a402-8ec84ce486cb/download3fdc7b41651299350522650338f5754dMD5220.500.12442/2303oai:bonga.unisimon.edu.co:20.500.12442/23032019-04-11 21:51:38.392metadata.onlyhttps://bonga.unisimon.edu.coDSpace UniSimonbibliotecas@biteca.comPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj48aW1nIGFsdD0iTGljZW5jaWEgQ3JlYXRpdmUgQ29tbW9ucyIgc3R5bGU9ImJvcmRlci13aWR0aDowIiBzcmM9Imh0dHBzOi8vaS5jcmVhdGl2ZWNvbW1vbnMub3JnL2wvYnktbmMvNC4wLzg4eDMxLnBuZyIgLz48L2E+PGJyLz5Fc3RhIG9icmEgZXN0w6EgYmFqbyB1bmEgPGEgcmVsPSJsaWNlbnNlIiBocmVmPSJodHRwOi8vY3JlYXRpdmVjb21tb25zLm9yZy9saWNlbnNlcy9ieS1uYy80LjAvIj5MaWNlbmNpYSBDcmVhdGl2ZSBDb21tb25zIEF0cmlidWNpw7NuLU5vQ29tZXJjaWFsIDQuMCBJbnRlcm5hY2lvbmFsPC9hPi4=
dc.title.eng.fl_str_mv Data Mining and Endocrine Diseases: A New Way to Classify?
title Data Mining and Endocrine Diseases: A New Way to Classify?
spellingShingle Data Mining and Endocrine Diseases: A New Way to Classify?
Data mining
Classification
Endocrine disease
Diabetes mellitus
Information analysis
title_short Data Mining and Endocrine Diseases: A New Way to Classify?
title_full Data Mining and Endocrine Diseases: A New Way to Classify?
title_fullStr Data Mining and Endocrine Diseases: A New Way to Classify?
title_full_unstemmed Data Mining and Endocrine Diseases: A New Way to Classify?
title_sort Data Mining and Endocrine Diseases: A New Way to Classify?
dc.creator.fl_str_mv Salazar, Juan
Espinoza, Cristobal
Mindiola, Andres
Bermudez, Valmore
dc.contributor.author.none.fl_str_mv Salazar, Juan
Espinoza, Cristobal
Mindiola, Andres
Bermudez, Valmore
dc.subject.eng.fl_str_mv Data mining
Classification
Endocrine disease
Diabetes mellitus
Information analysis
topic Data mining
Classification
Endocrine disease
Diabetes mellitus
Information analysis
description 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.
publishDate 2018
dc.date.accessioned.none.fl_str_mv 2018-10-03T19:39:20Z
dc.date.available.none.fl_str_mv 2018-10-03T19:39:20Z
dc.date.issued.none.fl_str_mv 2018-04
dc.type.eng.fl_str_mv article
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.identifier.issn.none.fl_str_mv 01884409
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/20.500.12442/2303
identifier_str_mv 01884409
url http://hdl.handle.net/20.500.12442/2303
dc.language.iso.eng.fl_str_mv eng
language eng
dc.rights.coar.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.license.spa.fl_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
rights_invalid_str_mv Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
http://purl.org/coar/access_right/c_abf2
dc.publisher.spa.fl_str_mv Elsevier
dc.source.eng.fl_str_mv Archives of Medical Research
dc.source.spa.fl_str_mv Vol. 49, No. 3 (2018)
institution Universidad Simón Bolívar
dc.source.uri.eng.fl_str_mv https://doi.org/10.1016/j.arcmed.2018.08.005
bitstream.url.fl_str_mv https://bonga.unisimon.edu.co/bitstreams/03b2241f-6a1d-416e-a402-8ec84ce486cb/download
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bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv DSpace UniSimon
repository.mail.fl_str_mv bibliotecas@biteca.com
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