Healthc Inform Res.  2010 Jun;16(2):77-81. 10.4258/hir.2010.16.2.77.

Diagnostic Analysis of Patients with Essential Hypertension Using Association Rule Mining

• Shin AM
• Lee IH
• Lee GH
• Park HJ
• Park HS
• Yoon KI
• Lee JJ
• Kim YN

• KMID: 2166570
• DOI: http://doi.org/10.4258/hir.2010.16.2.77

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.

Keyword

Hypertension; Diagnosis; Data Mining

MeSH Terms

Arm
Cerebral Infarction
Data Mining
Diabetes Mellitus
Diabetes Mellitus, Type 2
Humans
Hypertension
Mining

Figure

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  Figure 1 The analysis process.

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  Figure 2 The distribution of patients according to age.

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  Figure 3 Association graph by using web node. E11: non-insulin-dependent diabetes mellitus, E87: other disorders of fluid, electrolytes and acid-base balance, I10: essential hypertension, I21: acute myocardial infarction, I50: heart failure, I63: cerebral infarction, K21: gastrooesophageal reflux disease, N18: chronic renal failure.

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