Multimodal Alzheimer's disease classification through ensemble deep random vector functional link neural network.

ABSTRACT: Alzheimer's disease (AD) is a condition with a complex pathogenesis, sometimes hereditary, characterized by the loss of neurons and synapses, along with the presence of senile plaques and neurofibrillary tangles. Early detection, particularly among individuals at high risk, is critical for effective treatment or prevention, yet remains challenging due to data variability and incompleteness. Most current research relies on single data modalities, potentially limiting comprehensive staging of AD. This study addresses this gap by integrating multimodal data-including clinical and genetic information-using deep learning (DL) models, with a specific focus on random vector functional link (RVFL) networks, to enhance early detection of AD and mild cognitive impairment (MCI). Our findings demonstr

SUBMITTER: Henriquez PA 

PROVIDER: S-EPMC11784893 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature