Advancing Parkinson’s Disease Severity Prediction using Multimodal Convolutional Recursive Deep Belief Networks

Abstract

Parkinson’s disease (PD), a progressive neurological ailment predominantly affecting individuals over the age of 60, involves the gradual loss of dopamine-producing neurons. The challenges associated with the subjectivity, resource intensity, and limited efficacy of current diagnostic methods, including the Unified Parkinson’s Disease Rating Scale (UPDRS), neuroimaging, and genetic analysis, underscore the need for innovative approaches. This paper introduces a groundbreaking multimodal deep learning framework that integrates Recurrent Neural Networks (RNN-DBN) for precise feature selection and Convolutional Neural Networks (CNNs) for robust feature extraction, aiming to enhance the accuracy of PD severity prediction. The methodology synergistically incorporates genetic data, imaging data from MRI and PET scans, and clinical evaluations. CNNs effectively capture spatial and temporal patterns within each data modality, preserving inter-modal linkages. The proposed RNN-DBN architecture, by skillfully leveraging temporal dependencies, improves model interpretability and provides a clearer understanding of the progression of Parkinson’s disease symptoms. Evaluation across diverse PD datasets demonstrates superior predictive performance compared to existing methods. This multimodal deep learning framework holds the potential to revolutionize PD diagnosis and monitoring, offering physicians a valuable tool for assessing the condition’s severity. The integration of various data sources enhances the model’s accuracy, providing a holistic perspective on Parkinson’s disease progression. This, in turn, facilitates improved clinical decision-making and patient care. Notably, the implementation in Python achieves a remarkable accuracy of 94.87%, surpassing existing methods like EOFSC and CNN by 1.44%.