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Diagnostic analysis of patients with essential hypertension using association rule mining

A Mi Shin1, In Hee Lee, Gyeong Ho Lee

  • 1Department of Medical Informatics, School of Medicine, Keimyung University, Daegu, Korea.

Healthcare Informatics Research
|August 6, 2011
PubMed

Insights

Essential hypertension is strongly linked to non-insulin dependent diabetes mellitus and cerebral infarction. Association rule mining effectively identified these comorbidities in a large patient database.

Area of Science:

  • Medical Informatics
  • Data Mining
  • Cardiovascular Epidemiology

Background:

  • Essential hypertension is a prevalent cardiovascular condition.
  • Understanding comorbidities is crucial for effective patient management.
  • Association Rule Mining (ARM) offers a method to uncover hidden relationships in large datasets.

Purpose of the Study:

  • To analyze patient records for essential hypertension using Association Rule Mining (ARM).
  • To identify significant comorbidities associated with essential hypertension.

Main Methods:

  • Utilized Apriori modeling within the Clementine 12.0 program for ARM analysis.
  • Extracted and analyzed diagnostic data from 5,022 patients with essential hypertension (ICD code, I10).

Main Results:

  • Identified strong associations between essential hypertension, non-insulin dependent diabetes mellitus (NIDDM), and cerebral infarction.
  • NIDDM showed a support of 35.15% and confidence of 100% with essential hypertension.
  • Cerebral infarction demonstrated a support of 21.21% and confidence of 100% with essential hypertension.

Conclusions:

  • Essential hypertension is significantly associated with NIDDM and cerebral infarction.
  • ARM is a practical tool for exploring comorbidities in large clinical databases.
  • Findings highlight potential targets for integrated care strategies.
Abstract

Keywords:
Data MiningDiagnosisHypertension

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