Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors
Journal article
Poudel, Govinda, Barnett, Anthony, Akram, Muhammad, Martino, Erika, Knibbs, Luke, Anstey, Kaarin, Shaw, Jonathan and Cerin, Ester. (2022). Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors. International Journal of Environmental Research and Public Health. 19(17), pp. 1-14. https://doi.org/10.3390/ijerph191710977
Authors | Poudel, Govinda, Barnett, Anthony, Akram, Muhammad, Martino, Erika, Knibbs, Luke, Anstey, Kaarin, Shaw, Jonathan and Cerin, Ester |
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Abstract | The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34–97 years) (n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed (r2 = 0.43) and memory (r2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed (r2 = 0.29) but weakly predicted memory (r2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data. |
Keywords | physical activity; neighbourhood environment; sedentary behaviour; machine learning; built environment; processing speed; cognition; memory; sociodemographic; prediction |
Year | 01 Jan 2022 |
Journal | International Journal of Environmental Research and Public Health |
Journal citation | 19 (17), pp. 1-14 |
Publisher | MDPI AG |
ISSN | 1661-7827 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/ijerph191710977 |
PubMed ID | 36078704 |
Scopus EID | 2-s2.0-85137571466 |
PubMed Central ID | PMC9517821 |
Web address (URL) | https://www.mdpi.com/1660-4601/19/17/10977 |
Open access | Published as ‘gold’ (paid) open access |
Research or scholarly | Research |
Page range | 1-14 |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 02 Sep 2022 |
Publication process dates | |
Accepted | 31 Aug 2022 |
Deposited | 17 Jan 2023 |
Additional information | © 2022 by the authors. |
Place of publication | Switzerland |
https://acuresearchbank.acu.edu.au/item/8y94z/machine-learning-for-prediction-of-cognitive-health-in-adults-using-sociodemographic-neighbourhood-environmental-and-lifestyle-factors
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Publisher's version
OA_Poudel_2022_Machine_Learning_for_Prediction_of_Cognitive.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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