Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach

doi:10.4330/wjc.v18.i3.116217

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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

Observational Study

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): 116217
Published online Mar 26, 2026. doi: 10.4330/wjc.v18.i3.116217

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Figure 1 Phenotypic clusters identified by clinical profiling. The plot illustrates the four distinct patient clusters derived from hierarchical clustering of clinical and demographic variables. Cluster 1 younger, predominantly healthy individuals. Cluster 2 older, predominantly healthy individuals. Clusters 3 and 4: Patients with significant comorbidities but divergent profiles.

Citation: Karbovskaya AD, Marzoog BA, Stroeva A, Suvorov A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Ismailova SM, Poluboyarinova IV, Kopylov P. Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach. World J Cardiol 2026; 18(3): 116217
URL: https://www.wjgnet.com/1949-8462/full/v18/i3/116217.htm
DOI: https://dx.doi.org/10.4330/wjc.v18.i3.116217

Karbovskaya AD, Marzoog BA, Stroeva A, Suvorov A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Ismailova SM, Poluboyarinova IV, Kopylov P. Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach. World J Cardiol 2026; 18(3): 116217 [DOI: 10.4330/wjc.v18.i3.116217]

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