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

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.

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

“…Results from the study on Alzheimer's Disease Neuroimaging Initiative (ADNI) data showed promising outcomes in identifying patient-specific cognitive biomarkers and assessment tasks based on structural MRI data. Several tests, including Logical Memory (LM), AD Assessment Scale-Cognitive Behavior (ADAS-Cog), Rey Auditory Verbal Learning Test (RAVLT), and the Functional Assessment Questionnaire (FAQ), have been identified as the most reliable predictors for the automatic classification of these pathologies [47] with immediate word recall tasks [48]. Semantic verbal fluency (SVF) tests are regularly used to screen for MCI.…”