Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques
Machine learning methods, particularly the CatBoost model, demonstrate high accuracy in predicting early catheter ablation outcomes in AF patients. The developed model has the potential to improve patient care and decision-making by identifying patients most likely to benefit from the procedure. Further studies with larger sample sizes and external validation are warranted to assess the generalizability of this method for catheter ablation outcome prediction in AF patients.
Authors
- Zahra Ghasemi (ORCID: https://orcid.org/0000-0003-3131-1584)
- Amir Askarinejad (ORCID: https://orcid.org/0000-0003-2803-079X)
- Majid Haghjoo (ORCID: https://orcid.org/0000-0002-9963-6628)
- Erfan Kohansal (ORCID: https://orcid.org/0000-0002-4095-4824)
- Amirreza Sabahizadeh
Institutions
- Iran University of Medical Sciences (IR)
- Shiraz University of Medical Sciences (IR)
- Shaheed Rajaei Cardiovascular Medical and Research Center (IR)
- Persian Gulf University (IR)
Publication Details
- Journal
- BMC Cardiovascular Disorders
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1186/s12872-024-04367-z
- Citations
- 2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.80