This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (2491) Cardiac Arrhythmia, Atrial Fibrillation (87) Artificial Intelligence (1421) Machine Learning (1501) Clinical Information and Decision Making (1434) Decision Support for Health Professionals (1210) Wearable Devices and Sensors (903) Computer-Aided Diagnosis (135) Novel Sensors and Data Acquisition Methods in Cardiology (200)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47803, first published .
Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

Authors of this article:

Beau Bo-Sheng Chuang1 Author Orcid Image ;   Albert C Yang1, 2, 3 Author Orcid Image
Article Authors Cited by (1) Tweetations (2) Metrics

Journals

  1. Hussain S, Ahmad S, Wasid M. Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study. Computers in Biology and Medicine 2025;184:109342 View