| 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) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| 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 |
Clinical Trials Study |
| Article Title |
Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Basheer Abdualah Marzoog, Peter Chomakhidze, Daria Gognieva, Artemiy Silantyev, Alexander Suvorov, Magomed Abdullaev, Natalia Mozzhukhina, Darya Alexandrovna Filippova, Sergey Vladimirovich Kostin, Maria Kolpashnikova, Natalya Ershova, Nikolay Ushakov, Dinara Mesitskaya and Philipp Kopylov |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Government Assignment |
1023022600020-6 |
| RSF Grant |
24-15-00549 |
| Ministry of Science and Higher Education of the Russian Federation within the Framework of State Support for the Creation and Development of World-Class Research Center |
075-15-2022-304 |
|
| Corresponding Author |
Basheer Abdualah Marzoog, Associate Chief Physician, MD, Cardiology, Sechenov University, 8-2 Trubetskaya street, Moscow 119991, Moskva, Russia. marzug@mail.ru |
| Key Words |
Ischemic heart disease; Single lead electrocardiography; Computed tomography myocardial perfusion; Prevention; Risk factors; Stress test; Machine learning model |
| Core Tip |
Ischemic heart disease (IHD) remains the leading cause of mortality and disability globally. This returns to the poor used diagnostic methods, physical stress test (area under the receiver operating characteristic curve 50%). The current paper demonstrated that the machine learning model using the parameters of the single channel electrocardiogram have a diagnostic accuracy of 67% in IHD. Single lead electrocardiogram have the potential to diagnose IHD using machine learning models. |
| Publish Date |
2025-04-21 08:12 |
| Citation |
<p>Marzoog BA, Chomakhidze P, Gognieva D, Silantyev A, Suvorov A, Abdullaev M, Mozzhukhina N, Filippova DA, Kostin SV, Kolpashnikova M, Ershova N, Ushakov N, Mesitskaya D, Kopylov P. Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters. <i>World J Cardiol</i> 2025; 17(4): 104396</p> |
| URL |
https://www.wjgnet.com/1949-8462/full/v17/i4/104396.htm |
| DOI |
https://dx.doi.org/10.4330/wjc.v17.i4.104396 |
