AI-based integration of ECG biomarkers for assessing cardiac risk in type 2 diabetes mellitus with comorbid conditions for patient stratification.

ABSTRACT:

Introduction

The increasing prevalence of type 2 diabetes mellitus (T2DM) requires improved early detection strategies that integrate demographic, clinical, physiological, and pharmacological data. Electrocardiographic (ECG) biomarkers offer a non-invasive means to assess diabetes-related cardiac risk, particularly in individuals with hypertension (HT) and cardiovascular disease (CVD) comorbidities of diabetes.

Methods

ECG data from 581 subjects were categorized by glycemic status (healthy, prediabetes, T2DM) and comorbidities. Demographic, clinical, and pharmaceutical data were merged with 10 s and 5 min ECG recordings. SMOTE was used to correct class imbalance. Support Vector Machines (SVM) performed best among machine learning classifiers. Classification accuracy, sensiti

SUBMITTER: Savvopoulos S 

PROVIDER: S-EPMC12450978 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature