  Parkinson's Disease (PD) affects millions globally, impacting movement. Prior
research utilized deep learning for PD prediction, primarily focusing on
medical images, neglecting the data's underlying manifold structure. This work
proposes a multimodal approach encompassing both image and non-image features,
leveraging contrastive cross-view graph fusion for PD classification. We
introduce a novel multimodal co-attention module, integrating embeddings from
separate graph views derived from low-dimensional representations of images and
clinical features. This enables more robust and structured feature extraction
for improved multi-view data analysis. Additionally, a simplified contrastive
loss-based fusion method is devised to enhance cross-view fusion learning. Our
graph-view multimodal approach achieves an accuracy of 91% and an area under
the receiver operating characteristic curve (AUC) of 92.8% in five-fold
cross-validation. It also demonstrates superior predictive capabilities on
non-image data compared to solely machine learning-based methods.
