| ISSN |
1949-8462 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
|
| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
|
| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Cardiac & Cardiovascular Systems |
| Manuscript Type |
Observational Study |
| Article Title |
Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Anna Dmitrievna Karbovskaya, Basheer Abdullah Marzoog, Anastasia Stroeva, Alexander Suvorov, Peter Chomakhidze, Daria Gognieva, Natalia Kuznetsova, Abromavich Syrkin, Valentin V Fadeev, Sevindzh M Ismailova, Irina V Poluboyarinova and Philipp Kopylov |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Priority 2030 Program of the Ministry of Science and Higher Education of Russia |
03.000.B.163, 03.000. B. 166 |
| Government Assignment Application of Mass Spectrometry and Exhaled Air Emission Spectrometry for Cardiovascular Risk Stratification |
1023022600020-6 |
|
| Corresponding Author |
Basheer Abdullah Marzoog, MD, PhD, Researcher, Institute of Personalized Cardiology of The Center “Digital Biodesign and Personalized Healthcare” of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, 8-2 Trubetskaya Street, Moscow 119991, Russia. marzug@mail.ru |
| Key Words |
Diabetes mellitus; Machine learning model; Diagnosis; Single lead electrocardiography; Hyperglycemia; Metabolic syndrome |
| Core Tip |
This study introduces a crucial paradigm shift for non-invasive diabetes detection using electrocardiography. Instead of a one-size-fits-all model, we employed phenotypic clustering to disentangle the confounding effects of cardiovascular disease. We identified a specific patient profile (cluster 4: High diabetes prevalence with significant but non-severe comorbidities) where a machine learning model, analyzing single-lead electrocardiography features like T-wave morphology and atrial conduction, achieves optimal and clinically viable performance (area under the curve 0.88). This proves that diabetes-specific cardiac “whispers” are detectable, but only with a precision medicine approach that tailors diagnostics to distinct clinical phenotypes. |
| Publish Date |
2026-03-23 08:54 |
| Citation |
Karbovskaya AD, Marzoog BA, Stroeva A, Suvorov A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Ismailova SM, Poluboyarinova IV, Kopylov P. Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach. World J Cardiol 2026; 18(3): 116217 |
| URL |
https://www.wjgnet.com/1949-8462/full/v18/i3/116217.htm |
| DOI |
https://dx.doi.org/10.4330/wjc.v18.i3.116217 |
