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.
Health Informatics Research Group, Osnabrueck University of Applied Sciences, Germany.
Hendrik
H
Nienhoff
Health Informatics Research Group, Osnabrueck University of Applied Sciences, Germany.
Ursula
U
Huebner
Nuertingen-Geislingen University, Germany.
Andreas
A
Frey
Health Informatics Research Group, Osnabrueck University of Applied Sciences, Germany.
Mareike
M
Przysucha
Hamamatsu University School of Medicine, Japan.
Michio
M
Kimura
D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data; D066264:Datasets as Topic / Q000706:statistics & numerical data*; D003920:Diabetes Mellitus / Q000175:diagnosis* / Q000453:epidemiology*; D057286:Electronic Health Records / Q000145:classification / Q000706:statistics & numerical data*; D006801:Humans; D007564:Japan / Q000453:epidemiology; D062527:Meaningful Use / Q000706:statistics & numerical data*; D015995:Prevalence; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012680:Sensitivity and Specificity