High-Accuracy Classification of Parkinson's Disease Using Ensemble Machine Learning and Stabilometric Biomarkers.

ABSTRACT: Background: Accurate differentiation of Parkinson's disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2-3, on medication; mean age 66 years ± 2.9 years), all aged 60-80 years, participated. Stabilometric data were collect

SUBMITTER: Brizzi ACB 

PROVIDER: S-EPMC12472432 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature