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

Author

Listed:
  • 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
    Public Health Unit, Sydney Local Health District, Camperdown, NSW 2050, Australia)

  • Kaarin J. Anstey

    (School of Psychology, University of New South Wales, Sydney, NSW 2052, Australia
    UNSW Ageing Futures Institute, University of New South Wales, Sydney, NSW 2052, Australia
    Neuroscience Research Australia, Sydney, NSW 2031, 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)

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.

Suggested Citation

  • Govinda R. Poudel & Anthony Barnett & Muhammad Akram & Erika Martino & Luke D. Knibbs & Kaarin J. Anstey & Jonathan E. Shaw & Ester Cerin, 2022. "Machine Learning for Prediction of Cognitive Health in Adults Using Sociodemographic, Neighbourhood Environmental, and Lifestyle Factors," IJERPH, MDPI, vol. 19(17), pages 1-14, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10977-:d:905301
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    References listed on IDEAS

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    1. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    2. Liu, Shiqin & Higgs, Carl & Arundel, Jonathan & Boeing, Geoff & Cerdera, Nicholas & Moctezuma, David & Cerin, Ester & Adlakha, Deepti & Lowe, Melanie & Giles-Corti, Billie, 2021. "A Generalized Framework for Measuring Pedestrian Accessibility around the World Using Open Data," SocArXiv cua35, Center for Open Science.
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    Cited by:

    1. Yifan Qin & Jinlong Wu & Wen Xiao & Kun Wang & Anbing Huang & Bowen Liu & Jingxuan Yu & Chuhao Li & Fengyu Yu & Zhanbing Ren, 2022. "Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type," IJERPH, MDPI, vol. 19(22), pages 1-16, November.

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