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

Figure 4 Pathophysiological pathway of diabetes-induced cardiac electrophysiological changes detected by single-lead electrocardiography and machine learning. Chronic hyperglycemia initiates four core mechanisms: Autonomic neuropathy, myocardial fibrosis, ion channel dysfunction, and microvascular impairment. These collectively alter cardiac electrophysiology, generating specific electrocardiography biomarkers including T-wave flattening (↑), prolonged QT interval (↑), and conduction abnormalities (ventricular activation↑, QRSE4). Machine learning integration of these features enables diabetes detection, with optimal performance in high-prevalence, moderate-cardiovascular diseases populations (cluster 4; area under the curve = 0.880). AGE: Advanced glycation end products; ECG: Electrocardiography; Tfi: T-wave flattening; QTc: Prolonged QT interval; VAT: Ventricular activation.