A Survey of Transformer Graph Neural Network Graph Attention Network and Capsule Network Approaches for Cardiovascular Disease Prediction

Abstract

Cardiovascular disease is one of the important causes of morbidity and mortality in the world and early diagnosis and accurate prediction are required. The recent developments of Artificial Intelligence (AI) technologies have simplified the prediction of cardiovascular disease diagnosis by analyzing a variety of health-related data such as the analysis of an electrocardiogram (ECG), electronic health records (EHRs), medical images, data from wearable devices and even genomic data. The present review reviews advanced architectures of AI for cardiovascular prediction, including multimodal learning architectures, Capsule Networks (CapsNets), Graph Attention Networks, Graph Neural Networks and Transformer models. Methods that model specific temporal relationships in clinical data using a transformer architecture are effective for capturing temporal dependencies in sequential clinical data, whereas methods that model the relationships among various patients, diseases, and healthcare events using graphs are effective for modeling complex relationships. CapsNets are hierarchical feature representations for cardiovascular signal and image analysis. Moreover, multimodal AI enhances the predictability by combining multiple healthcare data sources, and Explainable Artificial Intelligence (XAI) techniques boost transparency and clinical trust. While there is great progress, there are issues with data heterogeneity and interpretability, computational complexity, external validation, privacy, and regulatory compliance. Clinically deployable cardiovascular prediction systems are likely to continue to be increasingly improved in future developments of multimodal learning, XAI, foundation models, and personalized medicine.