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August 26, 2025Neurology InternationalOpen Access

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

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Authors

ABAna Carolina Brisola BrizziONOsmar Pinto NetoRPRodrigo Cunha de Mello Pedreiro

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Overview

Ensemble machine learning classifies Parkinson's disease with 91% accuracy, highlighting stabilometric biomarkers.

Key Points

  • The ensemble model achieved a mean accuracy of 0.91, effectively distinguishing Parkinson's disease from healthy counterparts.
  • An area under the ROC curve of 0.97 indicates strong predictive validity for diagnosing Parkinson's disease.
  • A set of stabilometric parameters, including sway velocity and total sway path, were critical in classification performance.
  • This approach suggests machine learning can enhance clinical assessment, providing a non-invasive diagnostic tool.

Cite This Study

Brizzi et al. (2025) studied this question.

synapsesocial.com/papers/68af63ddad7bf08b1eae3e68https://doi.org/10.3390/neurolint17090133
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