J Appl Biomed 16:40-45, 2018 | DOI: 10.1016/j.jab.2017.10.005

A novel algorithm for identifying risk factors for rare events: Predicting transient ischemic attack in young patients with low-risk atrial fibrillation

Chieh-Yu Liu^a,b,*, Hui-Chun Chen^c

^a National Taipei University of Nursing and Health Sciences, College of Health Technology, Department of Speech Language Pathology and Audiology, Biostatistical Consulting Lab, Taipei, Taiwan

^b National Taipei University of Nursing and Health Sciences, College of Nursing. Department of Midwifery and Women Health Care, Taipei, Taiwan

^c National Taipei University of Nursing and Health Sciences, College of Nursing, Department of Nursing, Taipei, Taiwan

Identification of risk factors for transient ischemic attack (TIA) is crucial for patients with atrial fibrillation (AF). However, identifying risk factors in young patients with low-risk AF is difficult, because the incidence of TIA in such patients is very low, which would result in traditional multiple logistic regression not being able to successfully identify the risk factors in such patients. Therefore, a novel algorithm for identifying risk factors for TIA is necessary. We thus propose a novel algorithm, which combines multiple correspondence analysis and hierarchical cluster analysis and uses the Taiwan National Health Insurance Research Database, a population-based database, to determine risk factors in these patients. The results of this study can help clinicians or patients with AF in preventing TIA or stroke events as early as possible.

Keywords: Multiple correspondence analysis; Hierarchical cluster analysis; Atrial fibrillation; Transient ischemic attack; Stroke; Logistic regression

Received: April 14, 2017; Revised: July 6, 2017; Accepted: October 13, 2017; Published: February 1, 2018  Show citation