Title:Machine Learning Applications in the Study of Parkinson’s Disease: A
Systematic Review
Volume: 18
Issue: 7
Author(s): Jordi Martorell-Marugán*, Marco Chierici, Sara Bandres-Ciga, Giuseppe Jurman and Pedro Carmona-Sáez*
Affiliation:
- Data Science for Health Research Unit, Fondazione Bruno Kessler, Trento, 38123, Italy
- Department of Statistics and
Operational Research, University of Granada, Granada, 18071, Spain
- GENYO. Centre for Genomics and Oncological
Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, 18016, Spain
- Fundación para la Investigación Biosanitaria de Andalucía Oriental-Alejandro Otero (FIBAO), Granada, 18012,
Spain
- Department of Statistics and
Operational Research, University of Granada, Granada, 18071, Spain
- GENYO. Centre for Genomics and Oncological
Research: Pfizer, University of Granada, Andalusian Regional Government, PTS Granada, Granada, 18016, Spain
Keywords:
Parkinson’s disease, machine learning, deep learning, artificial intelligence, systematic review, bioinformatics.
Abstract:
Background: Parkinson’s disease is a common neurodegenerative disorder that has been
studied from multiple perspectives using several data modalities. Given the size and complexity of these
data, machine learning emerged as a useful approach to analyze them for different purposes. These
methods have been successfully applied in a broad range of applications, including the diagnosis of Parkinson’s
disease or the assessment of its severity. In recent years, the number of published articles that
used machine learning methodologies to analyze data derived from Parkinson’s disease patients have
grown substantially.
Objective: Our goal was to perform a comprehensive systematic review of the studies that applied machine
learning to Parkinson’s disease data.
Methods: We extracted published articles in PubMed, SCOPUS and Web of Science until March 15,
2022. After selection, we included 255 articles in this review.
Results: We classified the articles by data type and we summarized their characteristics, such as outcomes
of interest, main algorithms, sample size, sources of data and model performance.
Conclusion: This review summarizes the main advances in the use of Machine Learning methodologies
for the study of Parkinson’s disease, as well as the increasing interest of the research community in this
area.