Accuracy of Machine Learning Using the Montreal Cognitive Assessment for the Diagnosis of Cognitive Impairment in Parkinson’s Disease
- Author:
Junbeom JEON
1
;
Kiyong KIM
;
Kyeongmin BAEK
;
Seok Jong CHUNG
;
Jeehee YOON
;
Yun Joong KIM
Author Information
- Publication Type:1
- From:Journal of Movement Disorders 2022;15(2):132-139
- CountryRepublic of Korea
- Language:English
-
Abstract:
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.
