Machine Learning Analysis of Digital Clock Drawing Test Performance for Differential Classification of Mild Cognitive Impairment Subtypes Versus Alzheimer’s Disease
| Autor: | Muhammad Umer, Jacob R. Epifano, Nicholas Calzaretto, Victor Wasserman, Robi Polikar, Sean McGuire, David J. Libon, Russell Binaco, Sheina Emrani |
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| Rok vydání: | 2020 |
| Předmět: |
Male
First line Feature selection Disease Neuropsychological Tests Machine learning computer.software_genre Digital clock Machine Learning Alzheimer Disease Screening method Humans Medicine Cognitive Dysfunction Cognitive impairment Aged Aged 80 and over business.industry General Neuroscience Neuropsychology Psychiatry and Mental health Clinical Psychology Female Test performance Neurology (clinical) Artificial intelligence business computer |
| Zdroj: | Journal of the International Neuropsychological Society. 26:690-700 |
| ISSN: | 1469-7661 1355-6177 |
| DOI: | 10.1017/s1355617720000144 |
| Popis: | Objective:To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer’s disease (AD) using features obtained from the digital Clock Drawing Test (dCDT).Methods:dCDT protocols were administered to 163 patients diagnosed with AD(n= 59), amnestic MCI (aMCI;n= 26), combined mixed/dysexecutive MCI (mixed/dys MCI;n= 43), and patients without MCI (non-MCI;n= 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for “10 after 11.” A digital pen and custom software recorded patient’s drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy.Results:Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, ADversusnon-MCI = 91.42%; ADversusaMCI = 91.49%; ADversusmixed/dys MCI = 84.05%; aMCIversusmixed/dys MCI = 84.11%; aMCIversusnon-MCI = 83.44%; and mixed/dys MCIversusnon-MCI = 85.42%. A follow-up two-group non-MCIversusall MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25–125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy.Conclusion:Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes. |
| Databáze: | OpenAIRE |
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