Parkinson's Disease (PD), a progressive neurodegenerative disorder, poses significant diagnostic challenges, particularly in its early stages. This study proposes a novel hierarchical neurosignal processing framework to enhance the accuracy and timeliness of PD diagnosis. Leveraging multilevel signal decomposition, feature extraction, and machine learning classification, the proposed methodology systematically analyzes neurophysiological signals-such as EEG and EMG-capturing both spatial and temporal characteristics relevant to PD biomarkers. The hierarchical structure allows for refined signal denoising and targeted feature mapping across different neural frequency bands, significantly improving signal fidelity and diagnostic specificity. Experimental validation on benchmark datasets demonstrates that the proposed approach outperforms conventional flat processing models in terms of classification accuracy, sensitivity, and robustness. These findings suggest that hierarchical neurosignal processing can serve as a powerful diagnostic tool, paving the way for early intervention strategies and improved patient outcomes in Parkinson's Disease management.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction, cognitive decline, and various non-motor symptoms. Early detection and accurate monitoring of the disease remain critical for effective intervention and management. Recent advancements in machine learning (ML) have shown promise in identifying biomarkers that can assist in the diagnosis and progression tracking of Parkinson's Disease. This study explores the role of hierarchical machine learning models in decoding PD biomarkers, aiming to improve predictive accuracy and interpretability. Hierarchical models, which organize data processing in a multi-level structure, allow for better integration of complex, multi-dimensional biomedical data, such as neuroimaging, genetic, and clinical information. By employing techniques like deep learning, decision trees, and ensemble methods, hierarchical models can capture intricate relationships and patterns that traditional ML models often overlook. This research examines how hierarchical approaches enhance the identification of key PD biomarkers, including those associated with motor symptoms, neurodegeneration, and cognitive impairment. Furthermore, the study discusses the potential for these models to offer personalized predictions, paving the way for more tailored treatments. The results demonstrate that hierarchical machine learning models can significantly improve diagnostic accuracy, offering new insights into the molecular and physiological underpinnings of Parkinson's Disease.