Healthc Inform Res.  2013 Mar;19(1):25-32. 10.4258/hir.2013.19.1.25.

Association Rules to Identify Complications of Cerebral Infarction in Patients with Atrial Fibrillation

Affiliations
  • 1Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Korea.
  • 2Biomedical Informatics Technology Center, Keimyung University School of Medicine, Daegu, Korea. ynkim@dsmc.or.kr
  • 3Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, Korea.
  • 4Department of Internal Medicine, Keimyung University School of Medicine, Daegu, Korea.

Abstract


OBJECTIVES
The purpose of this study was to find risk factors that are associated with complications of cerebral infarction in patients with atrial fibrillation (AF) and to discover useful association rules among these factors.
METHODS
The risk factors with respect to cerebral infarction were selected using logistic regression analysis with the Wald's forward selection approach. The rules to identify the complications of cerebral infarction were obtained by using the association rule mining (ARM) approach.
RESULTS
We observed that 4 independent factors, namely, age, hypertension, initial electrocardiographic rhythm, and initial echocardiographic left atrial dimension (LAD), were strong predictors of cerebral infarction in patients with AF. After the application of ARM, we obtained 4 useful rules to identify complications of cerebral infarction: age (>63 years) and hypertension (Yes) and initial ECG rhythm (AF) and initial Echo LAD (>4.06 cm); age (>63 years) and hypertension (Yes) and initial Echo LAD (>4.06 cm); hypertension (Yes) and initial ECG rhythm (AF) and initial Echo LAD (>4.06 cm); age (>63 years) and hypertension (Yes) and initial ECG rhythm (AF).
CONCLUSIONS
Among the induced rules, 3 factors (the initial ECG rhythm [i.e., AF], initial Echo LAD, and age) were strongly associated with each other.

Keyword

Atrial Fibrillation; Cerebral Infarction; Risk Factors; Association Learning; Data Mining

MeSH Terms

Arm
Association Learning
Atrial Fibrillation
Cerebral Infarction
Data Mining
Electrocardiography
Humans
Hypertension
Logistic Models
Mining
Risk Factors
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