In recent years, progress in deep learning has significantly refined AD/MCI classification, but the relationship between functional connectivity changes and structural connectors remains to be created. To address this issue, this article proposes an inventive diagnostic system that utilizes the brain's effective connectivity network and integrates the Graph Attention Network (GAT) with the Long Short-Term Memory Network (LSTM). with the Long Short-Term Memory Organize (LSTM). By capturing brain interactions and dynamic changes, the framework can progress with demonstrative precision. Utilizing the Alzheimer's Malady Neuroimaging Activity (ADNI) dataset, the framework proved to be excellent at recognizing and predicting Alzheimer's disease, which illustrates the clinical potential it has. This research details the design, implementation and initial validation of the proposed method, emphasizing its effectiveness.
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