Development and validation of a machine learning model for diagnosis of ischemic heart disease using single-lead electrocardiogram parameters

doi:10.4330/wjc.v17.i4.104396

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Apr 26, 2025 (publication date) through Apr 21, 2025

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World Journal of Cardiology

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1949-8462

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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

Table 9 Ten most statically significant features

	Feature	LASSO absolute coefficient
1	q0_QTc	0.0221996
2	q0_Beta	0.0211857
3	q0_J80A	0.0179913
4	q0_Pan…30	0.0168176
5	q0_SA	0.013914
6	dltq_01_SA	0.0126805
7	dltq_01_Sbeta	0.0125996
8	q0_TA	0.0124491
9	dltq_01_RA	0.0116579
10	q0_Sbeta	0.0111864

Dltq_01 indicates the difference between the selected single-lead electrocardiography features immediately after the stress test (bicycle ergometry) minus the selected features before the stress test.

Citation: 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. World J Cardiol 2025; 17(4): 104396
URL: https://www.wjgnet.com/1949-8462/full/v17/i4/104396.htm
DOI: https://dx.doi.org/10.4330/wjc.v17.i4.104396