{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T18:32:44Z","timestamp":1790620364909,"version":"4.1.0"},"reference-count":84,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Neurodegenerative diseases (NDs), such as Alzheimer\u2019s disease (AD) and Parkinson\u2019s disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.<\/jats:p>","DOI":"10.3390\/s24248152","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T10:58:55Z","timestamp":1734951535000},"page":"8152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Recognizes Stages of Parkinson\u2019s Disease Using Magnetic Resonance Imaging"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0993-4630","authenticated-orcid":false,"given":"Artur","family":"Chudzik","sequence":"first","affiliation":[{"name":"Faculty of Computer Science, Polish-Japanese Academy of Information Technology, 86 Koszykowa Street, 02-008 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1177\/1073858406293182","article-title":"Small-World Brain Networks","volume":"12","author":"Bassett","year":"2006","journal-title":"Neuroscientist"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19219","DOI":"10.1073\/pnas.0609523103","article-title":"Small worlds inside big brains","volume":"103","author":"Sporns","year":"2006","journal-title":"Proc. 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