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An Analysis of Vocal Features for Parkinson's Disease Classification Using Evolutionary Algorithms.

Son V T Dao1, Zhiqiu Yu2, Ly V Tran1

  • 1School of Industrial Engineering and Management, International University, Vietnam National University, Ho Chi Minh City 700000, Vietnam.

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Summary

This study introduces an automated Parkinson's Disease (PD) detection method using voice analysis. Machine learning models optimized with Grey Wolf Optimization and Light Gradient Boosted Machine accurately classify PD from healthy individuals.

Keywords:
Parkinson’s diseasefeature subset selectiongrey wolf optimization

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Computational Science

Background:

  • Parkinson's Disease (PD) affects millions globally, causing significant movement impairments and impacting daily life.
  • Current diagnostic methods can be costly and invasive, highlighting the need for accessible screening tools.
  • Voice changes are a known symptom of PD, offering a potential avenue for non-invasive detection.

Purpose of the Study:

  • To develop and validate a machine learning model for the automatic detection of Parkinson's Disease using voice recordings.
  • To enhance the accuracy of PD detection by employing advanced feature selection and model optimization techniques.
  • To provide a cost-effective and accessible method for early identification of Parkinson's Disease.

Main Methods:

  • Utilized Grey Wolf Optimization (GWO) for effective feature selection from vocal recordings.
  • Employed Light Gradient Boosted Machine (LGBM) for classifying individuals with and without Parkinson's Disease.
  • Analyzed vocal patterns to identify discriminative features indicative of PD.

Main Results:

  • The proposed machine learning approach achieved highly competitive results in classifying Parkinson's Disease.
  • Feature selection using GWO and model optimization with LGBM significantly improved detection accuracy.
  • The method demonstrated strong potential for distinguishing between healthy individuals and those with PD based on voice data.

Conclusions:

  • The developed voice-based machine learning model offers a promising, non-invasive tool for Parkinson's Disease detection.
  • The integration of GWO and LGBM provides an efficient and accurate method for PD screening.
  • This approach has the potential for real-world implementation, aiding in early diagnosis and management of Parkinson's Disease.