Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model　- A Cross-Sectional Study.

ABSTRACT: Background: Atrial fibrillation (AF) is the most common arrhythmia and is associated with increased thromboembolic stroke risk and heart failure. Although various prediction models for AF risk have been developed using machine learning, their output cannot be accurately explained to doctors and patients. Therefore, we developed an explainable model with high interpretability and accuracy accounting for the non-linear effects of clinical characteristics on AF incidence. Methods and Results: Of the 489,073 residents who underwent specific health checkups between 2009 and 2018 and were registered in the Kanazawa Medical Association database, data were used for 5,378 subjects with AF and 167,950 subjects with normal electrocardiogram readings. Forty-seven clinical p

SUBMITTER: Kawakami M 

PROVIDER: S-EPMC8811230 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature