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July 26, 2025International Journal of Science for Global Sustainability

Application of machine learning models to classify Parkinson disease patients using accelerometer

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Authors

ARAmeenu Abdulhameed Rabiu RabiuIIIsmail IbrahimBBBuhari Bashir

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Overview

This research demonstrates improved classification accuracy for Parkinson disease using ensemble models and machine learning techniques.

Key Points

  • The ensemble model achieved an impressive accuracy of 65.69%, indicating its effectiveness in distinguishing patients from healthy individuals.
  • Frameworks like support vector machines and neural networks were employed to classify movement patterns in Parkinson disease patients.
  • Data was sourced from the University of Zaragoza Hospital, ensuring a rich diversity of participants, including both diagnosed and undiagnosed individuals.
  • Ensemble classifiers demonstrated enhanced diagnostic precision, suggesting a promising avenue for early detection and monitoring of Parkinson disease.

Cite This Study

Rabiu et al. (2025) studied this question.

synapsesocial.com/papers/68af4ec6ad7bf08b1ead7ff8https://doi.org/10.57233/ijsgs.v11i2.875
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Comprehensive Ensemble Machine Learning Model for Predicting Parkinson’s Disease Progression and Severity2024
  2. 2Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study2024 · 2 citations
  3. 3Optimized Multimodal Machine Learning Framework for Parkinson’s Disease Detection and Severity Analysis2026
  4. 4Personalized Data-Driven Robust Machine Learning Models to Differentiate Parkinson's Disease Patients Using Heterogeneous Risk Factors2025
  5. 5A Survey of Machine Learning Approaches for Parkinson’s Disease Prediction2024 · 1 citations