Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression

doi:10.4330/wjc.v18.i3.116115

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World Journal of Cardiology

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1949-8462

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Cardiol. Mar 26, 2026; 18(3): 116115
Published online Mar 26, 2026. doi: 10.4330/wjc.v18.i3.116115

Table 5 The coefficient estimates of selected factors from the final model, along with their 95%CI

Parameter	Estimated coefficient	Lower limit 95%CI	Upper limit 95%CI	Р value
Intercept	5.852	4.950	6.753	0.0000000
HFSNR	-0.058	-0.159	0.043	0.261
QRSfi	-0.001	-0.003	0.0009	0.253
T-wave flattening	0.0018	0.001	0.003	0.0000114
PpeakN	-0.0017	-0.004	0.0009	0.202

A weak to moderate correlation was observed between the true and predicted values (Spearman’s rho = 0.488, P < 0.001).

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Citation: Karbovskaya AD, Marzoog BA, Stroeva A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Poluboyarinova IV, Ismailova SM, Suvorov A, Kopylov P. Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression. World J Cardiol 2026; 18(3): 116115
URL: https://www.wjgnet.com/1949-8462/full/v18/i3/116115.htm
DOI: https://dx.doi.org/10.4330/wjc.v18.i3.116115

Karbovskaya AD, Marzoog BA, Stroeva A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Poluboyarinova IV, Ismailova SM, Suvorov A, Kopylov P. Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression. World J Cardiol 2026; 18(3): 116115 [DOI: 10.4330/wjc.v18.i3.116115]

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