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🔗 Source: open-access.bcu.ac.uk
📊 Size: 1.21 MB
📄 Pages: 17 pages
⬇️ Downloads: 33

Summary

This work proposes a comprehensive approach to enhance Parkinson's disease prediction using machine learning and feature selection methods. The approach uses several machine learning methods with different feature selection methods, including filter-based and wrapper-based methods, to analyze a dataset of 240 records with 46 acoustic features extracted from voice recordings of 80 patients. The experimental results show improvements when using a wrapper-based feature selection method with a K-NN classifier, achieving an accuracy of 88.33%. The results are comparable to and superior to other studies, demonstrating the effectiveness of the proposed approach in predicting Parkinson's disease.

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Description

This work proposes a comprehensive approach to enhance Parkinson's disease prediction using machine learning and feature selection methods.

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  • File Format: PDF
  • File Size: 1.21 MB
  • Pages: 17
  • Language: EN
  • Total Downloads: 33
  • Last Updated: 2 months ago

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