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

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...

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Bibliographic Details
Published in:	PloS one Vol. 15; no. 3; p. e0229460
Main Authors:	Orimaye, Sylvester Olubolu, Goodkin, Karl, Riaz, Ossama Abid, Salcedo, Jean-Maurice Miranda, Al-Khateeb, Thabit, Awujoola, Adeola Olubukola, Sodeke, Patrick Olumuyiwa
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 05-03-2020 Public Library of Science (PLoS)
Subjects:	Advertising executives Aging Algorithms Alzheimer's disease Alzheimers disease Analysis Behavioral sciences Biology and Life Sciences Cognitive ability Computer and Information Sciences Consortia Data mining Datasets Dementia Dementia disorders Diagnosis Diagnostic systems Disease control Diseases Education Educational assessment Evaluation Factor analysis Health care Impairment Language Learning algorithms Linguistics Machine learning Medical diagnosis Medical schools Medicine Medicine and Health Sciences Memory Neurodegenerative diseases Physical Sciences Psychiatry Race Research and Analysis Methods Sex Social Sciences Massachusetts Oregon United States > US
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Summary:	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 reliable AUC of 0.88 without controlling for age, sex, race, and education. Overall, the linguistic battery showed a better diagnostic result compared to the Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale (CDR), and a combination of MMSE and CDR.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0229460