Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)99-106
Number of pages8
JournalAlzheimer's & dementia (Amsterdam, Netherlands)
Volume4
DOIs
Publication statusPublished - 2016

Cite this

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title = "Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease",
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.",
author = "Meenakshi Dauwan and {van der Zande}, {Jessica J} and {van Dellen}, Edwin and Sommer, {Iris E C} and Philip Scheltens and Lemstra, {Afina W} and Stam, {Cornelis J}",
year = "2016",
doi = "10.1016/j.dadm.2016.07.003",
language = "English",
volume = "4",
pages = "99--106",
journal = "Alzheimer's & dementia (Amsterdam, Netherlands)",

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Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease. / Dauwan, Meenakshi; van der Zande, Jessica J; van Dellen, Edwin; Sommer, Iris E C; Scheltens, Philip; Lemstra, Afina W; Stam, Cornelis J.

In: Alzheimer's & dementia (Amsterdam, Netherlands), Vol. 4, 2016, p. 99-106.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Random forest to differentiate dementia with Lewy bodies from Alzheimer's disease

AU - Dauwan, Meenakshi

AU - van der Zande, Jessica J

AU - van Dellen, Edwin

AU - Sommer, Iris E C

AU - Scheltens, Philip

AU - Lemstra, Afina W

AU - Stam, Cornelis J

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

U2 - 10.1016/j.dadm.2016.07.003

DO - 10.1016/j.dadm.2016.07.003

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JO - Alzheimer's & dementia (Amsterdam, Netherlands)

JF - Alzheimer's & dementia (Amsterdam, Netherlands)

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