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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
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  • 135 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.
Validation of the Conversion between the Mini-Mental State Examination and Montreal Cognitive assessment in Korean Patients with Parkinson’s Disease
Ryul Kim, Han-Joon Kim, Aryun Kim, Mi-Hee Jang, Hyun Jeong Kim, Beomseok Jeon
J Mov Disord. 2018;11(1):30-34.   Published online January 11, 2018
DOI: https://doi.org/10.14802/jmd.17038
  • 8,973 View
  • 247 Download
  • 11 Web of Science
  • 16 Crossref
AbstractAbstract PDF
Objective
Two conversion tables between the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) have recently been established for Parkinson’s disease (PD). This study aimed to validate them in Korean patients with PD and to evaluate whether they could be influenced by educational level.
Methods
A total of 391 patients with PD who undertook both the Korean MMSE and the Korean MoCA during the same session were retrospectively assessed. The mean, median, and root mean squared error (RMSE) of the difference between the true and converted MMSE scores and the intraclass correlation coefficient (ICC) were calculated according to educational level (6 or fewer years, 7–12 years, or 13 or more years).
Results
Both conversions had a median value of 0, with a small mean and RMSE of differences, and a high correlation between the true and converted MMSE scores. In the classification according to educational level, all groups had roughly similar values of the median, mean, RMSE, and ICC both within and between the conversions.
Conclusion
Our findings suggest that both MMSE-MoCA conversion tables are useful instruments for transforming MoCA scores into converted MMSE scores in Korean patients with PD, regardless of educational level. These will greatly enhance the utility of the existing cognitive data from the Korean PD population in clinical and research settings.

Citations

Citations to this article as recorded by  
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  • Validation of MoCA-MMSE Conversion Scales in Korean Patients with Cognitive Impairments
    Young Ik Jung, Eun Hye Jeong, Heejin Lee, Junghee Seo, Hyun-Jeong Yu, Jin Y. Hong, Mun Kyung Sunwoo
    Dementia and Neurocognitive Disorders.2018; 17(4): 148.     CrossRef
The MMSE and MoCA for Screening Cognitive Impairment in Less Educated Patients with Parkinson’s Disease
Ji In Kim, Mun Kyung Sunwoo, Young H. Sohn, Phil Hyu Lee, Jin Y. Hong
J Mov Disord. 2016;9(3):152-159.   Published online September 21, 2016
DOI: https://doi.org/10.14802/jmd.16020
  • 20,543 View
  • 405 Download
  • 37 Web of Science
  • 36 Crossref
AbstractAbstract PDF
Objective
To explore whether the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) can be used to screen for dementia or mild cognitive impairment (MCI) in less educated patients with Parkinson’s disease (PD).
Methods
We reviewed the medical records of PD patients who had taken the Korean MMSE (K-MMSE), Korean MoCA (K-MoCA), and comprehensive neuropsychological tests. Predictive values of the K-MMSE and K-MoCA for dementia or MCI were analyzed in groups divided by educational level.
Results
The discriminative powers of the K-MMSE and K-MoCA were excellent [area under the curve (AUC) 0.86–0.97] for detecting dementia but not for detecting MCI (AUC 0.64–0.85). The optimal screening cutoff values of both tests increased with educational level for dementia (K-MMSE < 15 for illiterate, < 20 for 0.5–3 years of education, < 23 for 4–6 years, < 25 for 7–9 years, and < 26 for 10 years or more; K-MoCA < 7 for illiterate, < 13 for 0.5–3 years, < 16 for 4–6 years, < 19 for 7–9 years, < 20 for 10 years or more) and MCI (K-MMSE < 19 for illiterate, < 26 for 0.5–3 years, < 27 for 4–6 years, < 28 for 7–9 years, and < 29 for 10 years or more; K-MoCA < 13 for illiterate, < 21 for 0.5–3 years, < 23 for 4–6 years, < 25 for 7–9 years, < 26 for 10 years or more).
Conclusion
Both MMSE and MoCA can be used to screen for dementia in patients with PD, regardless of educational level; however, neither test is sufficient to discriminate MCI from normal cognition without additional information.

Citations

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JMD : Journal of Movement Disorders