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

BACKGROUND Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus (DM). AIM To investigate any association between single-lead electrocardiography (ECG) parameters and the diagnosis of DM. METHODS A single center study involved participants of Caucasian origin for the period between May 2, 2022 and August 23, 2025 with or without DM and aged ≥ 18 years. All participants participating in the study passed the cardiologist’s, random glucose measurement using a glucometer, single lead-ECG registration (using Cardio-Qvark®) and transthoracic echocardiography. Statistical analysis conducted using the R programming language (version 4.5). RESULTS The built logistic regression machine learning model demonstrated diagnostic performance in discriminating (area under the curve) type 1 DM 0.84 (95%CI: 0.76-0.91), type 2 DM 0.69 (95%CI: 0.61-0,76), and healthy control 0.82 (95%CI: 0.76-0.87). CONCLUSION The developed model demonstrates an association between single-lead ECG parameters and diabetes status that can support the clinical identification of individuals who would benefit from confirmatory testing. This is probably attributable to relatively stable and long-term physiological alterations associated with the state of the disease.