Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study

Abstract

The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.