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

Sylvester Olubolu Orimaye, Karl Goodkin, Ossama Abid Riaz, Jean Maurice Miranda Salcedo, Thabit Al-Khateeb, Adeola Olubukola Awujoola, Patrick Olumuyiwa Sodeke

Research output: Contribution to journalArticle

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 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.

Original languageEnglish (US)
Article numbere0229460
JournalPloS one
Volume15
Issue number3
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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    Orimaye, S. O., Goodkin, K., Riaz, O. A., Salcedo, J. M. M., Al-Khateeb, T., Awujoola, A. O., & Sodeke, P. O. (2020). A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease. PloS one, 15(3), [e0229460]. https://doi.org/10.1371/journal.pone.0229460