Using EEG Signals and AI to Predict Neurodegenerative Diseases
Using EEG Signals and AI to Predict Neurodegenerative Diseases
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Original abstract
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.