Clinical Trials Study
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World J Cardiol. Apr 26, 2025; 17(4): 104396
Published online Apr 26, 2025. doi: 10.4330/wjc.v17.i4.104396
Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters
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, Philipp Kopylov
Basheer Abdualah Marzoog, Peter Chomakhidze, Daria Gognieva, Artemiy Silantyev, Alexander Suvorov, Magomed Abdullaev, Philipp Kopylov, World-Class Research Center Digital Biodesign and Personalized Healthcare, I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991, Moscow, Russia
Natalia Mozzhukhina, Dinara Mesitskaya, University Clinical Hospital Number 1, Cardiology Department, I.M. Sechenov First Moscow State Medical University (Sechenov University), 8-2 Trubetskaya Street, 119991, Moscow, Russia
Darya Alexandrovna Filippova, Maria Kolpashnikova, Natalya Ershova, Nikolay Ushakov, Undergraduate Medical School student, Sechenov University, Moscow 119991, Moskva, Russia
Sergey Vladimirovich Kostin, Department of Plastic Surgery, Mordovia State University, Saransk 430005, Mordoviya, Russia
Author contributions: Marzoog BA contributed to writing, collecting and analyzing data, interpreting the results, and revising the final version of the manuscript; Suvorov A contributed to biostatistical analysis of the sample; Chomakhidze P, Gognieva D, Silantyev A, Suvorov A, Abdullaev M, Mozzhukhina N, Filippova DA, Kostin SV, Kolpashnikova M, Ershova N, Ushakov N, and Mesitskaya D revised the paper; Kopylov P revised the final version of the manuscript and contributed to the concept and development of the study; All authors read and approved the manuscript.
Supported by Government Assignment, No. 1023022600020-6; RSF Grant, No. 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, No. 075-15-2022-304.
Institutional review board statement: The study was approved by the Sechenov University, Russia, from “Ethics Committee Requirement No. 19-23 from 26.10.2023.”
Clinical trial registration statement: The study was registered on clinicaltrials.gov (NCT06181799).
Informed consent statement: All study participants or their legal guardian provided informed written consent about personal and medical data collection prior to study enrolment.
Conflict-of-interest statement: All authors have no conflicts of interest related to the manuscript.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
Data sharing statement: Not applicable.
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/
Corresponding author: Basheer Abdualah Marzoog, MD, Associate Chief Physician, Department of Cardiology, Sechenov University, 8-2 Trubetskaya Street, Moscow 119991, Moskva, Russia. marzug@mail.ru
Received: December 19, 2024
Revised: February 19, 2025
Accepted: March 31, 2025
Published online: April 26, 2025
Processing time: 123 Days and 14.8 Hours
Core Tip

Core Tip: Ischemic heart disease (IHD) remains the leading cause of mortality and disability globally. The diagnostic methods, including the physical stress test, have poor accuracy (50%) and specificity. The current paper demonstrated that the machine learning model using the parameters of the single-lead electrocardiogram had a diagnostic accuracy of 67% in IHD. Single-lead electrocardiogram has the potential to diagnose IHD using machine learning models.