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

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.

Figure 2 Top 15 features for predicting diabetes mellitus in cluster 4. The feature importance plot from the gradient boosting machine model shows the relative contribution of each variable. Tfi: T-wave flatness index; PpeakN: Negative P-wave peak amplitude; Rpeak: R-wave amplitude; Pst: P-wave start time; SDNN: Standard deviation of normal-to-normal intervals; HFQRS: High-frequency QRS; QTc: Corrected QT; QRSfi: QRS complex morphology; SA: Amplitude of the S wave; QRSE3: To the ranges set by the frequency grid of 2-4-8-16-32 Hz; J80A: Amplitude at point J+80 milliseconds, μV.