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

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. Keywords: Atrial Fibrillation, Cerebral Infarction, Risk Factors, Association Learning, Data Mining