IG-GCN: Empowering e-Health Services for Alzheimer’s Disease Prediction

Abstract

The rapid development of e-Health provides elderly consumers with more convenient medical services. Alzheimer’s disease is one of the major diseases that threaten the health of the elderly. Its early detection is vital for its effective treatment and management. In this study, an end-to-end model, individual-to-group graph convolutional network (IG-GCN), is proposed for AD early detection and abnormal brain region identification. Specifically, the proposed IG-GCN first learns the low-dimensional brain graph embeddings of individual brain networks, and then incorporates individual non-imaging information to construct an information-rich group network for all participants. Experimental results demonstrate that the proposed model surpasses baseline methods in AD prediction and can effectively identify abnormal brain regions and biomarkers at various stages of the disease. The brain network-based IG-GCN framework not only advances pathological research and early treatment of AD, but is also more amenable to integration with consumer electronics due to its low dimensionality and simplicity compared to traditional brain imaging data, offering a novel avenue for smart healthcare solutions within the realm of consumer electronics.