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

Figure 1 A network of factors demonstrating statistically significant differences in Holm-corrected pairwise comparisons are presented. The largest nodes (depicted in yellow) represent factors that exhibited significant differences across all three groups. Medium-sized nodes correspond to factors differing between any two groups, while the smallest nodes indicate factors significant for only a single group. On the left graph, the nodes are coloured (yellow or orange) according to their ability to differentiate between the types of diabetes. The right graph shows nodes that distinguish the control group from either type of diabetes (significance observed in either "no DM" vs "T1DM", "no DM" vs "T2DM", or across all three classes). The factors that did not appear in both graphs did not demonstrate statistically significant pairwise comparisons after multiplicity correction. DM: Diabetes mellitus; Tfi: T-wave flattening; Pfi: P-wave dispersion index; QTc: Prolonged QT interval; VAT: Ventricular activation.

Figure 2 Graphical representation of the diagnostic accuracy of the built model in the diagnosis of diabetes mellitus. AUC: Area under the curve; DM: Diabetes mellitus; ROC: Receiver operating characteristic.

Figure 3  Graphical representation of the predicted vs true values plot.

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.