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A study to classify non-dipper/dipper blood pressure pattern of type 2 diabetes mellitus patients without Holter device

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dc.contributor.authorsAltikardes Z.A., Erdal H., Baba A.F., Tezcan H., Fak A.S., Korkmaz H.
dc.date.accessioned2022-03-15T02:10:08Z
dc.date.accessioned2026-01-11T11:10:40Z
dc.date.available2022-03-15T02:10:08Z
dc.date.issued2014
dc.description.abstractThe aim of this study was to design an expert system to predict the Non-Dipping or Dipping pattern by using several basic clinical and laboratory data through an artificial intelligence algorithm. Data Mining is a technique which extracts information from data sets by using a combination of both statistical analysis methods and artificial intelligence algorithms. Also in this study, the decision tree and naivebayes classification algorithms of this technique were used. © 2014 IEEE.
dc.identifier.doi10.1109/WCCAIS.2014.6916555
dc.identifier.isbn9781479933518
dc.identifier.urihttps://hdl.handle.net/11424/247420
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2014 World Congress on Computer Applications and Information Systems, WCCAIS 2014
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectabpm
dc.subjectclassification
dc.subjectdiabetes
dc.subjectJ48
dc.subjectnon-dipper
dc.subjectweka
dc.titleA study to classify non-dipper/dipper blood pressure pattern of type 2 diabetes mellitus patients without Holter device
dc.typeconferenceObject
dspace.entity.typePublication
oaire.citation.title2014 World Congress on Computer Applications and Information Systems, WCCAIS 2014

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