Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study
An interpretable ML model based on RF accurately predicts AF recurrence post-ablation and outperforms conventional risk scores. This tool may enhance individualized patient counseling, follow-up strategy design, and resource allocation in clinical practice.
Authors
- Yicheng Wang (ORCID: https://orcid.org/0000-0002-6364-137X)
- Jian-Cheng Zhang (ORCID: https://orcid.org/0000-0002-8848-5748)
- Yi-Ming Peng
- Xiaolin Liao (ORCID: https://orcid.org/0000-0003-4601-948X)
- Hongyi Yang
- Jianquan Chen (ORCID: https://orcid.org/0009-0000-0585-9269)
- Zi-Ao Fan
Institutions
- Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
Publication Details
- Journal
- BMC Cardiovascular Disorders
- Published
- 2026-01-14
- DOI
- https://doi.org/10.1186/s12872-025-05489-8
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00