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September 10, 2025International Scientific Journal of Engineering and Management

A Machine Learning Approach to Predict Parkinson’s Disease Using Voting Classifier

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Authors

BBBandaru BhavanaCVCH. VASUNDHARA

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Overview

This study demonstrates improved disease prediction in Parkinson's using voice biomarkers, highlighting machine learning's potential.

Key Points

  • Our approach achieves an accuracy of 95% in predicting parkinson's disease using voice biomarkers.
  • Voice data collected from individuals with and without parkinson's disease is analyzed to enhance predictive accuracy.
  • The voting classifier combines XGBoost and support vector classifier, showcasing an advanced method for disease prediction.
  • Improved diagnostic tools could lead to more reliable parkinson's disease detection in clinical settings.

Cite This Study

Bhavana et al. (2025) studied this question.

synapsesocial.com/papers/68c1afcd54b1d3bfb60e7b59https://doi.org/10.55041/isjem04865
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Also Consider

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

  1. 1Early Detection of Parkinsons Using Machine Learning2024 · 4 citations
  2. 2Ensemble Machine Learning Approach for Parkinson’s Disease Detection Using Speech Signals2024 · 46 citations
  3. 3Pilot Study Employing a Machine Learning Approach as a Potential Method for Predicting Parkinson’s Disease Using Voice as a Digital Biomarker and the SHAP Approach for Feature Engineering2026
  4. 4Predictive Modelling for Parkinson's Disease Diagnosis using Biomedical Voice Measurements2024
  5. 5Dysphonic Voice Pattern Based Parkinson Disease Detection Using Machine Learning Models2024