Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease
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Publication date
2016
Authors
Dauwan, Meenakshi
van der Zande, Jessica J.
van Dellen, Edwin
Sommer, I. E.
Scheltens, Philip
Lemstra, Afina W.
Stam, Cornelis J.
Editors
Advisors
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Document Type
Article
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cc_by_nc_nd
Abstract
Introduction The aim of this study was to build a random forest classifier to improve the diagnostic accuracy in differentiating dementia with Lewy bodies (DLB) from Alzheimer's disease (AD) and to quantify the relevance of multimodal diagnostic measures, with a focus on electroencephalography (EEG). Methods A total of 66 DLB, 66 AD patients, and 66 controls were selected from the Amsterdam Dementia Cohort. Quantitative EEG (qEEG) measures were combined with clinical, neuropsychological, visual EEG, neuroimaging, and cerebrospinal fluid data. Variable importance scores were calculated per diagnostic variable. Results For discrimination between DLB and AD, the diagnostic accuracy of the classifier was 87%. Beta power was identified as the single-most important discriminating variable. qEEG increased the accuracy of the other multimodal diagnostic data with almost 10%. Discussion Quantitative EEG has a higher discriminating value than the combination of the other multimodal variables in the differentiation between DLB and AD.
Keywords
Alzheimer's disease, Beta power, Dementia with Lewy bodies, Diagnostic accuracy, EEG, Machine learning, Random forest, Clinical Neurology, Psychiatry and Mental health, Journal Article
Citation
Dauwan, M, van der Zande, J J, van Dellen, E, Sommer, I E C, Scheltens, P, Lemstra, A W & Stam, C J 2016, 'Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease', Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring, vol. 4, pp. 99-106. https://doi.org/10.1016/j.dadm.2016.07.003