Novel Classification of Atrial Fibrillation using Machine Learning
Approved Research ID: 58988
Approval date: September 1st 2020
Atrial fibrillation (AF) is the most common rhythm disorder affecting more than 33 million people worldwide. AF is associated with an increased risk of adverse cardiovascular outcomes and death. In order to identify populations that are at low risk and increased risk of developing adverse cardiac outcomes, several risk estimators have been proposed. However, those estimators have limitations, with suboptimal prediction capability. It is imperative to identify low-risk AF and high-risk AF accurately to improve cardiovascular outcomes and avoid unnecessary treatments and overburden medical resources. Machine learning has the distinct ability to handle vast amounts of data and classify them independently without manual categorization.
The purpose of this study is to reclassify AF using machine learning. Results from this study can lead to a better understanding of the disease pathology and improved clinical outcomes by tailored treatment strategies based on the new classification.