Immediate word recall in cognitive assessment can predict dementia using machine learning techniques

Michael Fayemiwo, T.A. Olowookere, O.O. Olaniyan, T.O. Ojewumi, I.S. Oyetade, S. Freeman, P. Jackson

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
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Abstract

Background
Dementia, one of the fastest-growing public health problems, is a cognitive disorder known to increase in prevalence as age increases. Several approaches had been used to predict dementia, especially in building machine learning (ML) models. However, previous research showed that most models developed had high accuracies, and they suffered from considerably low sensitivities. The authors discovered that the nature and the scope of the data used in this study had not been explored to predict dementia based on cognitive assessment using ML techniques. Therefore, we hypothesized that using word-recall cognitive features could help develop models for the prediction of dementia through ML techniques and emphasized assessing the models’ sensitivity performance.

Methods
Nine distinct experiments were conducted to determine which responses from either sample person (SP)’s or proxy’s responses in the “word-delay,” “tell-words-you-can-recall,” and “immediate-word-recall” tasks are essential in the prediction of dementia cases, and to what extent the combination of the SP’s or proxy’s responses can be helpful in the prediction of dementia. Four ML algorithms (K-nearest neighbors (KNN), decision tree, random forest, and artificial neural networks (ANN)) were used in all the experiments to build predictive models using data from the National Health and Aging Trends Study (NHATS).

Results
In the first scenario of experiments using “word-delay” cognitive assessment, the highest sensitivity (0.60) was obtained from combining the responses from both SP and proxies trained KNN, random forest, and ANN models. Also, in the second scenario of experiments using the “tell-words-you-can-recall” cognitive assessment, the highest sensitivity (0.60) was obtained by combining the responses from both SP and proxies trained KNN model. From the third set of experiments performed in this study on the use of “Word-recall” cognitive assessment, it was equally discovered that the use of combined responses from both SP and proxies trained models gave the highest sensitivity of 1.00 (as obtained from all the four models).

Conclusion
It can be concluded that the combination of responses in a word recall task as obtained from the SP and proxies in the dementia study (based on the NHATS dataset) is clinically useful in predicting dementia cases. Also, the use of “word-delay” and “tell-words-you-can-recall” cannot reliably predict dementia as they resulted in poor performances in all the developed models, as shown in all the experiments. However, immediate-word recall is reliable in predicting dementia, as seen in all the experiments. This, therefore, shows the significance of immediate-word-recall cognitive assessment in predicting dementia and the efficiency of combining responses from both SP and proxies in the immediate-word-recall task.
Original languageEnglish
Article number111
Pages (from-to)1-14
Number of pages14
JournalAlzheimer's Research & Therapy
Volume15
Issue number1
Early online date15 Jun 2023
DOIs
Publication statusPublished online - 15 Jun 2023

Bibliographical note

Funding Information:
We are very grateful to National Health and Aging Trends Study (NHATS), which provided access to the complete data and data user guide used in this study.

Funding Information:
A standardized dataset on dementia from the National Health and Aging Trends Study (NHATS) was obtained from the organization’s public data bank [] to carry out this study. NHATS is sponsored by the National Institute on Aging (grant number NIA U01AG032947) through a cooperative agreement with the Johns Hopkins Bloomberg School of Public Health []. To construct ML predictive models to predict dementia from the obtained dataset, four conventional classification ML algorithms, including K-nearest neighbor (KNN), decision tree (DT), random forest (RF), and artificial neural networks (ANN), were adopted. The purpose of selecting these algorithms is not to determine which ML model has the highest potential for the prediction of dementia; instead, it is to examine through the use of the ML models which cognitive assessment method(s) can best predict cases of dementia. An overview of the workflow of the study is illustrated in Fig. . As shown in the designed workflow, cognitive features are selected from the NHSAT dataset.

Publisher Copyright:
© 2023, The Author(s).

Keywords

  • Cognitive assessment
  • Dementia
  • Machine learning
  • Medical data analytics
  • Word recall

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