KMDS
E-submission
Search
- Page Path
- HOME > Search
Original 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
- 7,402 View
- 171 Download
- 13 Web of Science
- 10 Crossref
-
Abstract
PDF
Supplementary 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. -
Citations
Citations to this article as recorded by
- Prediction of Amyloid Positivity in Lewy Body Disease Using Early-Phase 18F-FP-CIT PET Images
Seok Jong Chung, Su Hong Kim, Seong Ho Jeong, Hye Sun Lee, Yun Joong Kim, Young H. Sohn, Yong Jeong, Phil Hyu Lee
Clinical Nuclear Medicine.2026; 51(1): 13. CrossRef - Handcrafted MRI radiomics of enlarged perivascular spaces and machine learning predict cognitive impairment and sleep disturbance in young adults
Li Li, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu
Scientific Reports.2026;[Epub] CrossRef - Assessment of mental and behavioural non-motor symptoms of Parkinson’s Disease using Artificial Intelligence (AI): a systematic review
Shantao Chloe Chou, Cen Cong, Rosiered Brownson-Smith, Madison Milne-Ives, Edward Meinert
Communications Medicine.2026;[Epub] CrossRef - Construction of a prediction and visualization system for cognitive impairment in elderly COPD patients based on self-assigning feature weights and residual evolution model
Wenwen Cheng, Chen Yu, Xiaohui Liu
Frontiers in Artificial Intelligence.2025;[Epub] CrossRef - Vascular and glymphatic dysfunction as drivers of cognitive impairment in Alzheimer's disease: Insights from computational approaches
Gehan Fatima, Akm Ashiquzzaman, Sang Seong Kim, Young Ro Kim, Hyuk-Sang Kwon, Euiheon Chung
Neurobiology of Disease.2025; 208: 106877. CrossRef - The Role of Machine Learning in Cognitive Impairment in Parkinson Disease: Systematic Review and Meta-Analysis
Yanyun Wu, Yangfan Cheng, Yi Xiao, Huifang Shang, Ruwei Ou
Journal of Medical Internet Research.2025; 27: e59649. CrossRef - Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis
Jing Wang, Yanfang Chen, Xiao Xie, Pengwei Wang, Hang Hu, Hongfang Han, Lihan Wang, Li Zhang
Frontiers in Aging Neuroscience.2025;[Epub] CrossRef - Clinical Outcome Assessments in Parkinson's Disease: A Scoping Review of Current Rating Scales and Future Needs
Evita Papathoma, Panagiota Tsitsi, Nirosen Vijiaratnam, Camila Aquino, Stephen R. Duma, Norbert Kovacs, Kigocha Lameck Okeng'o, Aparna Wagle Shukla, Roongroj Bhidayasiri, Tiago A. Mestre, Alvaro Sanchez Ferro, Alberto J. Espay, Michelle H.S. Tosin, Matej
Movement Disorders Clinical Practice.2025;[Epub] CrossRef - Comparing Montreal Cognitive Assessment Performance in Parkinson’s Disease Patients: Age- and Education-Adjusted Cutoffs vs. Machine Learning
Kyeongmin Baek, Young Min Kim, Han Kyu Na, Junki Lee, Dong Ho Shin, Seok-Jae Heo, Seok Jong Chung, Kiyong Kim, Phil Hyu Lee, Young H. Sohn, Jeehee Yoon, Yun Joong Kim
Journal of Movement Disorders.2024; 17(2): 171. CrossRef - Machine learning for the detection and diagnosis of cognitive impairment in Parkinson’s Disease: A systematic review
Callum Altham, Huaizhong Zhang, Ella Pereira, Farzin Hajebrahimi
PLOS ONE.2024; 19(5): e0303644. CrossRef
- Prediction of Amyloid Positivity in Lewy Body Disease Using Early-Phase 18F-FP-CIT PET Images
First
Prev
