Predicting Early recurrence of atrial fibrilation post-catheter ablation using machine learning techniques.

ABSTRACT:

Background

Catheter ablation is a common treatment for atrial fibrillation (AF), but recurrence rates remain variable. Predicting the success of catheter ablation is crucial for patient selection and management. This research seeks to create a machine learning model to forecast the early recurrence of atrial fibrillation following catheter ablation.

Methods

A prospective longitudinal study was conducted using data from the Iranian AF registry. The dataset included 402 consecutive AF patients who underwent radiofrequency catheter ablation. The primary outcome was early recurrence of AF within 3 months' post-ablation. Data preprocessing and feature selection were performed, followed by the development and evaluation of various machine learning models. The CatBoost model was se

SUBMITTER: Askarinejad A 

PROVIDER: S-EPMC11657212 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature