Skip Navigation
Skip to contents

JMD : Journal of Movement Disorders

OPEN ACCESS
SEARCH
Search

Author index

Page Path
HOME > Browse Articles > Author index
Search
Kyeongmin Baek 1 Article
Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson’s Disease
Junbeom Jeon, Kiyong Kim, Kyeongmin Baek, Seok Jong Chung, Jeehee Yoon, Yun Joong Kim
J Mov Disord. 2022;15(2):132-139.   Published online May 26, 2022
DOI: https://doi.org/10.14802/jmd.22012
  • 614 View
  • 67 Download
AbstractAbstract PDFSupplementary Material
Objective
The Montreal Cognitive Assessment (MoCA) is recommended for assessing general cognition in Parkinson’s disease (PD). Several cutoffs of MoCA scores for diagnosing PD with cognitive impairment (PD-CI) have been proposed, with varying sensitivity and specificity. This study investigated the utility of machine learning algorithms using MoCA cognitive domain scores for improving diagnostic performance for PD-CI.
Methods
In total, 2,069 MoCA results were obtained from 397 patients with PD enrolled in the Parkinson’s Progression Markers Initiative database with a diagnosis of cognitive status based on comprehensive neuropsychological assessments. Using the same number of MoCA results randomly sampled from patients with PD with normal cognition or PD-CI, discriminant validity was compared between machine learning (logistic regression, support vector machine, or random forest) with domain scores and a cutoff method.
Results
Based on cognitive status classification using a dataset that permitted sampling of MoCA results from the same individual (n = 221 per group), no difference was observed in accuracy between the cutoff value method (0.74 ± 0.03) and machine learning (0.78 ± 0.03). Using a more stringent dataset that excluded MoCA results (n = 101 per group) from the same patients, the accuracy of the cutoff method (0.66 ± 0.05), but not that of machine learning (0.74 ± 0.07), was significantly reduced. Inclusion of cognitive complaints as an additional variable improved the accuracy of classification using the machine learning method (0.87–0.89).
Conclusion
Machine learning analysis using MoCA domain scores is a valid method for screening cognitive impairment in PD.

JMD : Journal of Movement Disorders