Authors - K Bhavish Raju, K Musadiq Pasha, Mohammed Saqlain, Nishaan Padanthaya, Jayashree R Abstract - Neuro-degenerative disorders, particularly Alzheimer’s Disease (AD), pose a significant challenge in early diagnosis and severity assessment due to overlapping symptoms with conditions such as Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) conditions. Accurate differentiation between these stages is essential for timely intervention but remains difficult due to the progressive and heterogeneous nature of these disorders. Traditional machine learning models struggle to effectively integrate diverse data modalities, such as medical imaging (MRI) and clinical tabular data. This study proposes Hypergraph Neural Networks (HyperGNNs) based framework to enhance multi-modal classification and disease severity modeling. By representing complex patient relationships as hypergraphs, our approach aims to improve diagnostic accuracy, reduce misdiagnosis, and provide an interpretable framework for understanding disease progression. To ensure clinical transparency, we incorporate explainability techniques such as SHAP and Grad-CAM to ensure model transparency, enabling clinicians to understand key features influencing predictions. The model will be evaluated on standard neuro-imaging datasets and clinical records, offering potential applications in personalized medicine and early intervention strategies.
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