Machine learning for Parkinson's disease: a comprehensive review of datasets, algorithms, and challenges.

Journal: NPJ Parkinson's disease
Published Date: Jul 1, 2025

Abstract

Parkinson's disease (PD) is a devastating neurological ailment affecting both mobility and cognitive function, posing considerable problems to the health of the elderly across the world. The absence of a conclusive treatment underscores the requirement to investigate cutting-edge diagnostic techniques to improve patient outcomes. Machine learning (ML) has the potential to revolutionize PD detection by applying large repositories of structured data to enhance diagnostic accuracy. 133 papers published between 2021 and April 2024 were reviewed using a systematic literature review (SLR) methodology, and subsequently classified into five categories: acoustic data, biomarkers, medical imaging, movement data, and multimodal datasets. This comprehensive analysis offers valuable insights into the applications of ML in PD diagnosis. Our SLR identifies the datasets and ML algorithms used for PD diagnosis, as well as their merits, limitations, and evaluation factors. We also discuss challenges, future directions, and outstanding issues.

Authors

  • Sahar Shokrpour
    Department of Computer Engineering, ST.C., Islamic Azad University, Tehran, Iran.
  • AmirMehdi MoghadamFarid
    Department of Computer Science, Michigan Technological University, Houghton, MI, USA.
  • Sepideh Bazzaz Abkenar
    Department of Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran.
  • Mostafa Haghi Kashani
    Department of Computer Engineering, ShQ.C., Islamic Azad University, Shahr-e Qods, Iran. mh.kashani@iau.ac.ir.
  • Mohammad Akbari
    Toxoplasmosis Research Center, Mazandaran University of Medical Sciences, Sari, Iran.
  • Mostafa Sarvizadeh
    The Advocate Center for Clinical Research, Ayatollah Yasrebi Hospital, Kashan, Iran.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals