Result: Optimization and Cross-Validation of Graph Neural Networks for the Diagnosis of Alzheimer's Disease

This work focuses on optimizing and cross-validating Graph Neural Networks (GNNs) for the diagnosis of Alzheimer's Disease (AD) using Electroencephalography (EEG) data. GNNs have shown promise in analyzing EEG signals for improved AD diagnosis accuracy. The study involves preprocessing EEG data, constructing a graph representation of functional connectivity, and optimizing GNN models through hyperparameter tuning and architecture selection. Rigorous cross-validation is conducted to ensure reliable performance. The experimental results demonstrate the efficacy of GNNs in diagnosing AD using EEG data, outperforming traditional machine learning approaches. This research aims to contribute to advancing AD diagnosis based on EEG and holds potential for early detection and intervention in patient care.