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Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study

BACKGROUND: Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE: This study aims to use classification techniques to identify the...

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Autores principales: Rankin, Debbie, Black, Michaela, Flanagan, Bronac, Hughes, Catherine F, Moore, Adrian, Hoey, Leane, Wallace, Jonathan, Gill, Chris, Carlin, Paul, Molloy, Anne M, Cunningham, Conal, McNulty, Helene
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527918/
https://www.ncbi.nlm.nih.gov/pubmed/32936084
http://dx.doi.org/10.2196/20995
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author Rankin, Debbie
Black, Michaela
Flanagan, Bronac
Hughes, Catherine F
Moore, Adrian
Hoey, Leane
Wallace, Jonathan
Gill, Chris
Carlin, Paul
Molloy, Anne M
Cunningham, Conal
McNulty, Helene
author_facet Rankin, Debbie
Black, Michaela
Flanagan, Bronac
Hughes, Catherine F
Moore, Adrian
Hoey, Leane
Wallace, Jonathan
Gill, Chris
Carlin, Paul
Molloy, Anne M
Cunningham, Conal
McNulty, Helene
author_sort Rankin, Debbie
collection PubMed
description BACKGROUND: Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE: This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. METHODS: Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. RESULTS: In the classification of a low RBANS score (<70), our models performed well (F(1) score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F(1) score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. CONCLUSIONS: The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients.
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spelling pubmed-75279182020-10-15 Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study Rankin, Debbie Black, Michaela Flanagan, Bronac Hughes, Catherine F Moore, Adrian Hoey, Leane Wallace, Jonathan Gill, Chris Carlin, Paul Molloy, Anne M Cunningham, Conal McNulty, Helene JMIR Med Inform Original Paper BACKGROUND: Machine learning techniques, specifically classification algorithms, may be effective to help understand key health, nutritional, and environmental factors associated with cognitive function in aging populations. OBJECTIVE: This study aims to use classification techniques to identify the key patient predictors that are considered most important in the classification of poorer cognitive performance, which is an early risk factor for dementia. METHODS: Data were used from the Trinity-Ulster and Department of Agriculture study, which included detailed information on sociodemographic, clinical, biochemical, nutritional, and lifestyle factors in 5186 older adults recruited from the Republic of Ireland and Northern Ireland, a proportion of whom (987/5186, 19.03%) were followed up 5-7 years later for reassessment. Cognitive function at both time points was assessed using a battery of tests, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), with a score <70 classed as poorer cognitive performance. This study trained 3 classifiers—decision trees, Naïve Bayes, and random forests—to classify the RBANS score and to identify key health, nutritional, and environmental predictors of cognitive performance and cognitive decline over the follow-up period. It assessed their performance, taking note of the variables that were deemed important for the optimized classifiers for their computational diagnostics. RESULTS: In the classification of a low RBANS score (<70), our models performed well (F(1) score range 0.73-0.93), all highlighting the individual’s score from the Timed Up and Go (TUG) test, the age at which the participant stopped education, and whether or not the participant’s family reported memory concerns to be of key importance. The classification models performed well in classifying a greater rate of decline in the RBANS score (F(1) score range 0.66-0.85), also indicating the TUG score to be of key importance, followed by blood indicators: plasma homocysteine, vitamin B6 biomarker (plasma pyridoxal-5-phosphate), and glycated hemoglobin. CONCLUSIONS: The results suggest that it may be possible for a health care professional to make an initial evaluation, with a high level of confidence, of the potential for cognitive dysfunction using only a few short, noninvasive questions, thus providing a quick, efficient, and noninvasive way to help them decide whether or not a patient requires a full cognitive evaluation. This approach has the potential benefits of making time and cost savings for health service providers and avoiding stress created through unnecessary cognitive assessments in low-risk patients. JMIR Publications 2020-09-16 /pmc/articles/PMC7527918/ /pubmed/32936084 http://dx.doi.org/10.2196/20995 Text en ©Debbie Rankin, Michaela Black, Bronac Flanagan, Catherine F Hughes, Adrian Moore, Leane Hoey, Jonathan Wallace, Chris Gill, Paul Carlin, Anne M Molloy, Conal Cunningham, Helene McNulty. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 16.09.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Rankin, Debbie
Black, Michaela
Flanagan, Bronac
Hughes, Catherine F
Moore, Adrian
Hoey, Leane
Wallace, Jonathan
Gill, Chris
Carlin, Paul
Molloy, Anne M
Cunningham, Conal
McNulty, Helene
Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title_full Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title_fullStr Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title_full_unstemmed Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title_short Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
title_sort identifying key predictors of cognitive dysfunction in older people using supervised machine learning techniques: observational study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527918/
https://www.ncbi.nlm.nih.gov/pubmed/32936084
http://dx.doi.org/10.2196/20995
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