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Prediction of Decline in Global Cognitive Function Using Machine Learning with Feature Ranking of Gait and Physical Fitness Outcomes in Older Adults

Author

Listed:
  • Byungjoo Noh

    (Department of Kinesiology, Jeju National University, Jeju 63243, Korea
    These authors contributed equally to this manuscript.)

  • Hyemin Yoon

    (Department of Management Information Systems, Dong-A University, Busan 49315, Korea
    These authors contributed equally to this manuscript.)

  • Changhong Youm

    (Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea
    These authors contributed equally to this manuscript.)

  • Sangjin Kim

    (Department of Management Information Systems, Dong-A University, Busan 49315, Korea
    These authors contributed equally to this manuscript.)

  • Myeounggon Lee

    (Department of Health and Human Performance, University of Houston, Houston, TX 77004, USA)

  • Hwayoung Park

    (Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea)

  • Bohyun Kim

    (Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea)

  • Hyejin Choi

    (Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Korea)

  • Yoonjae Noh

    (Department of Management Information Systems, Dong-A University, Busan 49315, Korea)

Abstract

Gait and physical fitness are related to cognitive function. A decrease in motor function and physical fitness can serve as an indicator of declining global cognitive function in older adults. This study aims to use machine learning (ML) to identify important features of gait and physical fitness to predict a decline in global cognitive function in older adults. A total of three hundred and six participants aged seventy-five years or older were included in the study, and their gait performance at various speeds and physical fitness were evaluated. Eight ML models were applied to data ranked by the p -value (LP) of linear regression and the importance gain (XI) of XGboost. Five optimal features were selected using elastic net on the LP data for men, and twenty optimal features were selected using support vector machine on the XI data for women. Thus, the important features for predicting a potential decline in global cognitive function in older adults were successfully identified herein. The proposed ML approach could inspire future studies on the early detection and prevention of cognitive function decline in older adults.

Suggested Citation

  • Byungjoo Noh & Hyemin Yoon & Changhong Youm & Sangjin Kim & Myeounggon Lee & Hwayoung Park & Bohyun Kim & Hyejin Choi & Yoonjae Noh, 2021. "Prediction of Decline in Global Cognitive Function Using Machine Learning with Feature Ranking of Gait and Physical Fitness Outcomes in Older Adults," IJERPH, MDPI, vol. 18(21), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11347-:d:667219
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    References listed on IDEAS

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    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Byungjoo Noh & Changhong Youm & Myeounggon Lee & Hwayoung Park, 2020. "Associating Gait Phase and Physical Fitness with Global Cognitive Function in the Aged," IJERPH, MDPI, vol. 17(13), pages 1-11, July.
    3. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    4. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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