The Role of Hierarchical Machine Learning Models in Decoding Parkinson's Disease Biomarkers

The Role of Hierarchical Machine Learning Models in Decoding Parkinson's Disease Biomarkers | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 15 May 2025 V1 Latest version Share on The Role of Hierarchical Machine Learning Models in Decoding Parkinson's Disease Biomarkers Author : Daniel TONY 0009-0004-6387-2831 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174733740.09136024/v1 140 views 88 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (untitled document (43).pdf) Download 1.24 MB Information & Authors Information Version history V1 Version 1 15 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keyword parkinson's disease neurosignal precision hierarchical classification biomedical signal processing machine learning Authors Affiliations Daniel TONY 0009-0004-6387-2831 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 140 views 88 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Daniel TONY. The Role of Hierarchical Machine Learning Models in Decoding Parkinson's Disease Biomarkers. Authorea . 15 May 2025. 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