PMID- 37322550 OWN - NLM STAT- MEDLINE DCOM- 20230619 LR - 20231208 IS - 1758-9193 (Electronic) VI - 15 IP - 1 DP - 2023 Jun 15 TI - Immediate word recall in cognitive assessment can predict dementia using machine learning techniques. PG - 111 LID - 10.1186/s13195-023-01250-5 [doi] LID - 111 AB - 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. CI - (c) 2023. The Author(s). FAU - Fayemiwo, Michael Adebisi AU - Fayemiwo MA AD - Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria. AD - School of Nursing, University of Northern British Columbia, Prince George, British Columbia, Canada. AD - School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, Northern Ireland, UK. FAU - Olowookere, Toluwase Ayobami AU - Olowookere TA AD - Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria. olowookereta@run.edu.ng. FAU - Olaniyan, Oluwabunmi Omobolanle AU - Olaniyan OO AD - Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria. FAU - Ojewumi, Theresa Omolayo AU - Ojewumi TO AD - Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria. FAU - Oyetade, Idowu Sunday AU - Oyetade IS AD - Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria. FAU - Freeman, Shannon AU - Freeman S AD - School of Nursing, University of Northern British Columbia, Prince George, British Columbia, Canada. FAU - Jackson, Piper AU - Jackson P AD - Department of Computing Science, Thompson Rivers University, Kamloops, British Columbia, Canada. LA - eng GR - DaSHI-001/BC SUPPORT Unit Methods Cluster Project Award/ GR - DaSHI-001/BC SUPPORT Unit Methods Cluster Project Award/ PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20230615 PL - England TA - Alzheimers Res Ther JT - Alzheimer's research & therapy JID - 101511643 SB - IM MH - Humans MH - *Cognition Disorders/epidemiology MH - *Cognitive Dysfunction MH - Machine Learning MH - *Dementia/diagnosis MH - Cognition PMC - PMC10268524 OTO - NOTNLM OT - Cognitive assessment OT - Dementia OT - Machine learning OT - Medical data analytics OT - Word recall COIS- The authors declare no competing interests. EDAT- 2023/06/16 01:08 MHDA- 2023/06/19 13:08 PMCR- 2023/06/15 CRDT- 2023/06/15 23:45 PHST- 2022/10/12 00:00 [received] PHST- 2023/05/29 00:00 [accepted] PHST- 2023/06/19 13:08 [medline] PHST- 2023/06/16 01:08 [pubmed] PHST- 2023/06/15 23:45 [entrez] PHST- 2023/06/15 00:00 [pmc-release] AID - 10.1186/s13195-023-01250-5 [pii] AID - 1250 [pii] AID - 10.1186/s13195-023-01250-5 [doi] PST - epublish SO - Alzheimers Res Ther. 2023 Jun 15;15(1):111. doi: 10.1186/s13195-023-01250-5.