IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1271-d1380861.html
   My bibliography  Save this article

Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis

Author

Listed:
  • Tho Nguyen

    (Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA)

  • Ladda Thiamwong

    (College of Nursing, University of Central Florida, Orlando, FL 32816, USA)

  • Qian Lou

    (Department of Computer Science, University of Central Florida, Orlando, FL 32816, USA)

  • Rui Xie

    (Department of Statistics and Data Science, University of Central Florida, Orlando, FL 32816, USA
    College of Nursing, University of Central Florida, Orlando, FL 32816, USA)

Abstract

While existing research has identified diverse fall risk factors in adults aged 60 and older across various areas, comprehensively examining the interrelationships between all factors can enhance our knowledge of complex mechanisms and ultimately prevent falls. This study employs a novel approach—a mixed undirected graphical model (MUGM)—to unravel the interplay between sociodemographics, mental well-being, body composition, self-assessed and performance-based fall risk assessments, and physical activity patterns. Using a parameterized joint probability density, MUGMs specify the higher-order dependence structure and reveals the underlying graphical structure of heterogeneous variables. The MUGM consisting of mixed types of variables (continuous and categorical) has versatile applications that provide innovative and practical insights, as it is equipped to transcend the limitations of traditional correlation analysis and uncover sophisticated interactions within a high-dimensional data set. Our study included 120 elders from central Florida whose 37 fall risk factors were analyzed using an MUGM. Among the identified features, 34 exhibited pairwise relationships, while COVID-19-related factors and housing composition remained conditionally independent from all others. The results from our study serve as a foundational exploration, and future research investigating the longitudinal aspects of these features plays a pivotal role in enhancing our knowledge of the dynamics contributing to fall prevention in this population.

Suggested Citation

  • Tho Nguyen & Ladda Thiamwong & Qian Lou & Rui Xie, 2024. "Unveiling Fall Triggers in Older Adults: A Machine Learning Graphical Model Analysis," Mathematics, MDPI, vol. 12(9), pages 1-18, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1271-:d:1380861
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1271/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/12/9/1271/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1271-:d:1380861. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.