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Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation
26
Zitationen
11
Autoren
2022
Jahr
Abstract
Background: Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF. Objectives: This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF. Methods: The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort. Results: < 0.001). Conclusions: The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.
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