Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors

Govinda R. Poudel (), Anthony Barnett, Muhammad Akram, Erika Martino, Luke D. Knibbs, Kaarin J. Anstey, Jonathan E. Shaw and Ester Cerin
Additional contact information
Govinda R. Poudel: Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
Anthony Barnett: Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
Muhammad Akram: Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia
Erika Martino: Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
Luke D. Knibbs: School of Public Health, The University of Sydney, Sydney, NSW 2006, Australia
Kaarin J. Anstey: School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia
Jonathan E. Shaw: Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
Ester Cerin: Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, VIC 3065, Australia

IJERPH, 2022, vol. 19, issue 17, 1-14

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 ( r 2 = 0.43) and memory ( r 2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed ( r 2 = 0.29) but weakly predicted memory ( r 2 = 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 (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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