Explaining graph convolutional network predictions for clinicians-An explainable AI approach to Alzheimer's disease classification.

ABSTRACT:

Introduction

Graph-based representations are becoming more common in the medical domain, where each node defines a patient, and the edges signify associations between patients, relating individuals with disease and symptoms in a node classification task. In this study, a Graph Convolutional Networks (GCN) model was utilized to capture differences in neurocognitive, genetic, and brain atrophy patterns that can predict cognitive status, ranging from Normal Cognition (NC) to Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Elucidating model predictions is vital in medical applications to promote clinical adoption and establish physician trust. Therefore, we introduce a decomposition-based explanation meth

SUBMITTER: Tekkesinoglu S 

PROVIDER: S-EPMC10801225 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature