A machine learning-based model for predicting paroxysmal and persistent atrial fibrillation based on EHR.

ABSTRACT:

Background

There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors.

Methods

We collected demographic data, medication use, serological indicators, and baseline cardiac ultrasound data of all included subjects, totaling 50 variables. The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. We built a prediction mode

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11792530 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature