Improving Early Prediction of Type 2 Diabetes Mellitus with ECG-DiaNet: A Multimodal Neural Network Leveraging Electrocardiogram and Clinical Risk Factors
This work addresses early detection of Type 2 Diabetes Mellitus for clinical and community health settings, particularly in underrepresented Middle Eastern populations, but is incremental as it combines existing modalities.
This study tackled the problem of early prediction of Type 2 Diabetes Mellitus by developing ECG-DiaNet, a multimodal deep learning model that integrates ECG features with clinical risk factors, achieving an AUROC of 0.845 and outperforming unimodal models with statistical significance.
Type 2 Diabetes Mellitus (T2DM) remains a global health challenge, underscoring the need for early and accurate risk prediction. This study presents ECG-DiaNet, a multimodal deep learning model that integrates electrocardiogram (ECG) features with clinical risk factors (CRFs) to enhance T2DM onset prediction. Using data from Qatar Biobank (QBB), we trained and validated models on a development cohort (n=2043) and evaluated performance on a longitudinal test set (n=395) with five-year follow-up. ECG-DiaNet outperformed unimodal ECG-only and CRF-only models, achieving a higher AUROC (0.845 vs 0.8217) than the CRF-only model, with statistical significance (DeLong p<0.001). Reclassification metrics further confirmed improvements: Net Reclassification Improvement (NRI=0.0153) and Integrated Discrimination Improvement (IDI=0.0482). Risk stratification into low-, medium-, and high-risk groups showed ECG-DiaNet achieved superior positive predictive value (PPV) in high-risk individuals. The model's reliance on non-invasive and widely available ECG signals supports its feasibility in clinical and community health settings. By combining cardiac electrophysiology and systemic risk profiles, ECG-DiaNet addresses the multifactorial nature of T2DM and supports precision prevention. These findings highlight the value of multimodal AI in advancing early detection and prevention strategies for T2DM, particularly in underrepresented Middle Eastern populations.