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KMID : 0603720100160020077
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Journal of Korean Society of Medical Informatics
2010 Volume.16 No. 2 p.77 ~ p.81
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Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining
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Shin A-Mi
Lee In-Hee
Lee Kyung-Ho
Park Hee-Joon
Park Hyoung-Seob
Youn Kyung-Il
Lee Jung-Jeung
Kim Youn-Nyun
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Abstract
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Objectives: The purpose of this study was to analyze the records of patients diagnosed with essential hypertension using association rule mining (ARM).
Methods: Patients with essential hypertension (ICD code, I10) were extracted from a hos-pital’s data warehouse and a data mart constructed for analysis. Apriori modeling of the ARM method and web node in the Clementine 12.0 program were used to analyze patient data.
Results: Patients diagnosed with essential hypertension totaled 5,022 and the diagnostic data extracted from those patients numbered 53,994. As a result of the web node, essential hyperten-sion, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction were shown to be associated. Based on the results of ARM, NIDDM (support, 35.15%; confidence, 100%) and cerebral infarction (support, 21.21%; confidence, 100%) were determined to be important diseases associated with essential hypertension.
Conclusions: Essential hypertension was strongly associated with NIDDM and cerebral infarction. This study demonstrated the practicality of ARM in co-morbidity studies using a large clinic database.
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KEYWORD
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Hypertension, Diagnosis, Data Mining
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