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 discrimi

SUBMITTER: Dauwan M 

PROVIDER: S-EPMC5050257 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature