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

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Listed:
  • Sylvester Olubolu Orimaye
  • Karl Goodkin
  • Ossama Abid Riaz
  • Jean-Maurice Miranda Salcedo
  • Thabit Al-Khateeb
  • Adeola Olubukola Awujoola
  • Patrick 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 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.

Suggested Citation

  • Sylvester Olubolu Orimaye & Karl Goodkin & Ossama Abid Riaz & Jean-Maurice Miranda Salcedo & Thabit Al-Khateeb & Adeola Olubukola Awujoola & Patrick Olumuyiwa Sodeke, 2020. "A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer’s disease," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-18, March.
  • Handle: RePEc:plo:pone00:0229460
    DOI: 10.1371/journal.pone.0229460
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    References listed on IDEAS

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    1. Alison Abbott, 2011. "Dementia: A problem for our age," Nature, Nature, vol. 475(7355), pages 2-4, July.
    2. Jin Hyuk Lee & John Huber Jr., 2011. "Multiple imputation with large proportions of missing data: How much is too much?," United Kingdom Stata Users' Group Meetings 2011 23, Stata Users Group.
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