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

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.