CardioGPT-ViT: Explainable Deep Learning Models With Multimodal Data for Coronary Artery Disease and Heart Failure on Edge Consumer Devices

Cardiovascular diseases (CVDs) are the most common causes of death in the world, especially coronary artery disease (CAD) and heart failure (HF). Prevention and sustained monitoring in the early stages are essential, yet conventional diagnostic methods are expensive, invasive, and limited to the clinical environment. Recent developments in consumer electronics (CE), including smartwatches and smartphones, are providing new opportunities for real-time, non-invasive cardiovascular monitoring. Nevertheless, the implementation of deep learning (DL) on such devices poses challenges for explainability, computational efficiency, and privacy. This paper introduces CardioGPT-ViT, a novel multimodal, explainable DL model for detecting CAD and HF on edge devices. The model combines Vision Transformers (ViT), GPT-style attention, Mamba state-space modeling, and a small You Only Look Once (YOLO) detection head and incorporates the explainability functionality of clinical trust. CardioGPT-ViT was trained and tested on three benchmark datasets: PulseDB, UCI Heart Disease, and an external electrocardiography (ECG)/photoplethysmography (PPG) cohort. The framework includes SHapley Additive exPlanations (SHAP), Local explainable Model-Agnostic Explanation (LIME), attention maps, and Gradient-weighted Class Activation Mapping (Grad-CAM) to explain results. Quantization, pruning, and latency/energy profiling were used to verify the feasibility of edge deployment on consumer devices. It was compared to performance with recent baseline models across convolutional neural networks (CNNs), ViTs, and state-space architectures. CardioGPT-ViT also scored over 0.90 across all datasets, reaching a maximum of 0.962 on PulseDB. The model was found to be more accurate (92.8%) and robust and had real-time feasibility (latency of less than 50 ms) and low energy consumption (less than 80 mJ per inference). Explainability results correlated with clinical biomarkers, increasing trust. CardioGPT-ViT provides a clinically explainable, resource-efficient, and edge-deployable solution to next-generation cardiovascular care, connecting the gap between consumer electronics and long-term preventive healthcare.