title: Association rules to identify complications of cerebral infarction in patients with atrial fibrillation.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Medical Informatics, Keimyung University School of Medicine, Daegu, Korea.
authors.forename: Sun-Ju
authors.initials: SJ
authors.lastname: Jung
authors.forename: Chang-Sik
authors.initials: CS
authors.lastname: Son
authors.forename: Min-Soo
authors.initials: MS
authors.lastname: Kim
authors.forename: Dae-Joon
authors.initials: DJ
authors.lastname: Kim
authors.forename: Hyoung-Seob
authors.initials: HS
authors.lastname: Park
authors.forename: Yoon-Nyun
authors.initials: YN
authors.lastname: Kim
keywords: Association Learning; Atrial Fibrillation; Cerebral Infarction; Data Mining; Risk Factors