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

Beau Bo-Sheng Chuang   1 * ;   Albert C Yang   1, 2, 3 * , MD, PhD

1 School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

2 Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan

3 Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan

*all authors contributed equally

Corresponding Author:

  • Albert C Yang, MD, PhD
  • Digital Medicine and Smart Healthcare Research Center
  • National Yang Ming Chiao Tung University
  • No 155, Li-Nong St, Sec.2
  • Beitou District
  • Taipei, 112304
  • Taiwan
  • Phone: 886 228267995
  • Fax: 886 228267389
  • Email: accyang@nycu.edu.tw