A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease

PloS One
Sylvester Olubolu OrimayePatrick Olumuyiwa Sodeke

Abstract

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD). We hypothesized that an independent linguistic battery comprising of only the language components or subtests of popular test batteries could give a better clinical diagnosis for MCI-AD compared to using an exhaustive battery of tests. As such, we combined multiple clinical datasets and performed Exploratory Factor Analysis (EFA) to extract the underlying linguistic constructs from a combination of the Consortium to Establish a Registry for Alzheimer's disease (CERAD), Wechsler Memory Scale (WMS) Logical Memory (LM) I and II, and the Boston Naming Test. Furthermore, we trained a machine-learning algorithm that validates the clinical relevance of the independent linguistic battery for differentiating between patients with MCI-AD and cognitive healthy control individuals. Our EFA identified ten linguistic variables with distinct underlying linguistic constructs that show Cronbach's alpha of 0.74 on the MCI-AD group and 0.87 on the healthy control group. Our machine learning evaluation showed a robust AUC of 0.97 when controlled for age, sex, race, and education, and a clinically ...Continue Reading

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Citations

Oct 27, 2020·Frontiers in Neuroscience·Lulu ZhangUNKNOWN Alzheimer’s Disease Neuroimaging Initiative (ADNI)

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Software Mentioned

RStudio
e1071 R
pROC
Statistical Analysis Software ( SAS )
gplots
e1071

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