This is an open access article distributedunder the terms of the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproductionin any medium,provided the original author and source are credited. 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.
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Orimaye, Sylvester Olubolu; Goodkin, Karl; Riaz, Ossama Abid; Salcedo, Jean Maurice Miranda; Al-Khateeb, Thabit; Awujoola, Adeola Olubukola; and Sodeke, Patrick Olumuyiwa. 2020. A Machine Learning-Based Linguistic Battery for Diagnosing Mild Cognitive Impairment Due to Alzheimer's Disease. PLoS ONE. Vol.15(3). https://doi.org/10.1371/journal.pone.0229460 PMID: 32134942