Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters
The single-lead ECG has a relatively higher diagnostic accuracy compared with bicycle ergometry by using machine learning models, but the difference was not statistically significant. However, further investigations are required to uncover the hidden capabilities of single-lead ECG in IHD diagnosis.
Authors
- Natalia Mozzhukhina (ORCID: https://orcid.org/0009-0002-7334-6945)
- Nikolay Ushakov
- Daria Gognieva (ORCID: https://orcid.org/0000-0002-0451-2009)
- P. Sh. Chomakhidze (ORCID: https://orcid.org/0000-0003-1485-6072)
- А. Yu. Suvorov (ORCID: https://orcid.org/0000-0002-2224-0019)
- Philipp Kopylov (ORCID: https://orcid.org/0000-0001-5124-6383)
- S. V. Kostin (ORCID: https://orcid.org/0000-0003-2549-1021)
- Н В Ершова
- Magomed Abdullaev (ORCID: https://orcid.org/0000-0001-9369-9890)
- Basheer Abdullah Marzoog (ORCID: https://orcid.org/0000-0001-5507-2413)
- Darya Filippova
- D. F. Mesitskaya (ORCID: https://orcid.org/0000-0003-3541-6041)
- Artemiy Silantyev
- Maria Kolpashnikova
Institutions
- Sechenov University (RU)
- National Research Mordovia State University (RU)
Publication Details
- Journal
- World Journal of Cardiology
- Published
- 2025-04-21
- DOI
- https://doi.org/10.4330/wjc.v17.i4.104396
- Citations
- 7
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 9.72