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Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining

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Author(s)
박희준박형섭김윤년이중정윤경일
Alternative Author(s)
Park, Hee JunPark, Hyoung SeobKim, Yoon NyunLee, Jung JeungYoon, Kyung Il
Issued Date
2010
Abstract
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 hospital'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 hypertension,
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.
Keywords: Hypertension, Diagnosis, Data Mining
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