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Restricted Versus Unrestricted Search Space : Experience from Mining a Large Japanese Database

  • The aim of this study was to investigate whether standard Big Data mining methods lead to clinically useful results. An association analysis was performed using the apriori algorithm to discover associations among co-morbidities of diabetes patients. Selected data were further analyzed by using k-means clustering with age, long-term blood sugar and cholesterol values. The association analysis led to a multitude of trivial rules. Cluster analysis detected clusters of well and badly managed diabetes patients both belonging to different age groups. The study suggests the usage of cluster analysis on a restricted space to come to meaningful results.

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Metadaten
Author:Mareike PrzysuchaORCiD, Ursula Hertha HübnerORCiD, Hendrik Nienhoff, Andreas Frey, Michio KimuraORCiD
Title (English):Restricted Versus Unrestricted Search Space : Experience from Mining a Large Japanese Database
URN:urn:nbn:de:bsz:959-opus-20915
DOI:https://doi.org/10.3233/978-1-61499-564-7-1072
ISBN:978-1-61499-563-0
ISBN:978-1-61499-564-7
Parent Title (English):MEDINFO 2015 : eHealth-enabled Health
Publisher:IOS Press
Place of publication:Amsterdam, Berlin, Washington (DC)
Document Type:Conference Proceeding
Language:English
Year of Completion:2015
Release Date:2021/05/27
Page Number:1
First Page:1072
Note:
MEDINFO 2015 - eHealth-enabled Health, 19.08. - 23.08.2015, São Paulo (Brazil)
Faculties:Fakultät WiSo
DDC classes:600 Technik, Medizin, angewandte Wissenschaften / 610 Medizin, Gesundheit
Review Status:Veröffentlichte Fassung/Verlagsversion
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International