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

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...

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Bibliographic Details
Published in:Mathematics (Basel) 2024-05, Vol.12 (9), p.1271
Main Authors: Nguyen, Tho, Thiamwong, Ladda, Lou, Qian, Xie, Rui
Format: Article
Language:English
Subjects:
Adults
Aging
Analysis
Balance
Body composition
Chronic illnesses
Composition
Correlation analysis
COVID-19
Disease prevention
Distribution (Probability theory)
Exercise
fall risks
Falls
Falls (Accidents)
Fatalities
Forecasts and trends
Machine learning
Medical research
Medicine, Experimental
mixed graphical models
Mortality
older adults
Older people
Public health
Questionnaires
Risk analysis
Risk assessment
Risk factors
Sociodemographics
undirected graphical models
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Summary: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 (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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math12091271