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Parkinson’s disease is a progressive brain disorder that affects motor coordination, making early diagnosis a recurring medical imperative. Based on this, this essay proposes an analysis and comparison of Gaussian Bayesian algorithms and Principal Component Analysis (PCA) strategies for diagnosing the disease. The aim is to direct the implementation of these machine learning models and strategies towards classifying Parkinson’s disease diagnosis and meeting the aforementioned medical.

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Gaussian Bayesian Algorithm Analysis and Principal Component Analysis for Parkinson’s Disease Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Wesley Rodrigues de Sousa,
  • Joyce Pereira Oliveira,
  • Daniel Santos da Silva,
  • Douglas Holanda do Carmo,
  • Renato William R. Souza,
  • Victor Hugo C. de Albuquerque

摘要

Parkinson’s disease is a progressive brain disorder that affects motor coordination, making early diagnosis a recurring medical imperative. Based on this, this essay proposes an analysis and comparison of Gaussian Bayesian algorithms and Principal Component Analysis (PCA) strategies for diagnosing the disease. The aim is to direct the implementation of these machine learning models and strategies towards classifying Parkinson’s disease diagnosis and meeting the aforementioned medical.