SCALABLE PARKINSONS DISEASE PREDICTION USING MATHEMATICAL MODELING, HILBERT TRANSFORMS, AND TRANSFORMER-BASED DEEP LEARNING

Parkinsons disease (PD) is a progressive neurological disorder necessitating early and precise diagnosis for effective therapy. However, traditional machine learning and convolutional neural network (CNN) methods generally have problems when used on heterogeneous biomedical data since they don't scale well, don't generalize well, and are harder to understand. To tackle these issues, this research presents a scalable hybrid system that combines mathematical modeling, Hilbert transform-based spatial embedding, and transformer-based deep learning architectures for predicting Parkinson's disease. The suggested Hilbert-based embedding adds biologically inspired spatial correlations that keep structural information and make features more stable. To efficiently capture both local and global dependencies, these improved features are subsequently processed using advanced transformer architectures including Swin Transformer and Vision Transformer (ViT). Testing the proposed framework on multimodal datasets that include spiral drawings, wave patterns, and functional MRI (fMRI) pictures shows that it is more accurate, precise, and recall than traditional CNN and machine learning models. The Swin Transformer with Hilbert embedding had the best performance, with an accuracy of 97.96%. This shows that it is more general and more robust. The findings demonstrate that the suggested mathematically based framework offers a scalable, interpretable, and clinically significant approach for the early prediction of Parkinson’s disease.