Using EEG Signals and AI to Predict Neurodegenerative Diseases

Diseases like Alzheimer’s and Parkinson’s pose significant challenges because of their complex and progressive characteristics. Addressing these conditions requires advanced diagnostic methods that support value-based care. This study proposes NeuroPredictNet, a deep learning-based predictive framework designed for early and accurate disease identification. NeuroPredictNet integrates multimodal data—EEG, neuroimaging, genetic, and clinical features—through an attention-based fusion mechanism that dynamically weights modality contributions. We introduce the Adaptive Knowledge Integration Strategy (AKIS) to enhance model robustness by addressing modality-specific noise, data imbalance, and temporal consistency. Experimental evaluations on four benchmark datasets demonstrate that our method achieves superior prediction accuracy and interpretability compared to state-of-the-art approaches. These results underscore the framework’s clinical potential in supporting personalized, value-based neurological care.