Optimizing graph neural network architectures for schizophrenia spectrum disorder prediction using evolutionary algorithms.
Journal:
Computer methods and programs in biomedicine
Published Date:
Sep 11, 2024
Abstract
BACKGROUND AND OBJECTIVE: The accurate diagnosis of schizophrenia spectrum disorder plays an important role in improving patient outcomes, enabling timely interventions, and optimizing treatment plans. Functional connectivity analysis, utilizing functional magnetic resonance imaging data, has been demonstrated to offer invaluable biomarkers conducive to clinical diagnosis. However, previous studies mainly focus on traditional machine learning methods or hand-crafted neural networks, which may not fully capture the spatial topological relationship between brain regions.