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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
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    References listed on IDEAS

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    1. Epskamp, Sacha & Cramer, Angélique O.J. & Waldorp, Lourens J. & Schmittmann, Verena D. & Borsboom, Denny, 2012. "qgraph: Network Visualizations of Relationships in Psychometric Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i04).
    2. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    3. Mikyong Byun & Jiyeon Kim & Ji Eun Kim, 2021. "Physical and Psychological Factors Contributing to Incidental Falls in Older Adults Who Perceive Themselves as Unhealthy: A Cross-Sectional Study," IJERPH, MDPI, vol. 18(7), pages 1-12, April.
    4. Renoa Choudhury & Joon-Hyuk Park & Chitra Banarjee & Ladda Thiamwong & Rui Xie & Jeffrey R. Stout, 2023. "Associations of Mutually Exclusive Categories of Physical Activity and Sedentary Behavior with Body Composition and Fall Risk in Older Women: A Cross-Sectional Study," IJERPH, MDPI, vol. 20(4), pages 1-16, February.
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