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Predicting fall risk in elderly ındividuals: a comparative analysis of machine learning models using patient characteristics, functional balance tests and computerized dynamic posturography

Published online by Cambridge University Press:  11 November 2024

Emre Soylemez Open the ORCID record for Emre Soylemez [Opens in a new window]  and
Suna Tokgoz-Yilmaz Open the ORCID record for Suna Tokgoz-Yilmaz [Opens in a new window]
Emre Soylemez*
Affiliation:
Department of Audiometry, Vocational School of Health Services, Karabuk University, Karabuk, Turkey Institute of Health Sciences, Audiology and Speech Disorders, Ankara University, Ankara, Turkey
Suna Tokgoz-Yilmaz
Affiliation:
Department of Audiology, Faculty of Health Sciences, Ankara University, Ankara, Turkey Audiology, Balance and Speech Disorders Unit, Medical Faculty, Ankara University, Ankara, Turkey
*
Corresponding author: Emre Soylemez; Email: [email protected]
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Abstract

Objectives

This study aimed to predict the risk of falling using patient characteristics, computerized dynamic posturography and functional balance tests in machine learning.

Methods

One hundred twenty elderly individuals were included in this study. The fall status, physical characteristics and medical history of individuals were investigated. Pure tone audiometry test, simple functional balance tests and sensory organization test were applied to the individuals.

Results

The machine learning model that incorporated co-morbidities, physical characteristics and functional balance tests achieved a 100 per cent accuracy in predicting fall risk. Models using only co-morbidities and physical characteristics, functional balance tests or the sensory organization test had accuracies of 87.5 per cent, 83.34 per cent and 91.66 per cent, respectively.

Conclusion

Advanced balance systems are not always necessary to assess fall risk. Instead, fall risk can be effectively determined using simple balance tests, co-morbidities, and patient characteristics in machine learning.

Keywords

balanceelderly ındividualsfall riskmachine learningposturography
Type
Main Article
Information
The Journal of Laryngology & Otology , First View , pp. 1 - 9
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of J.L.O. (1984) LIMITED.

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Footnotes

Emre Soylemez takes responsibility for the integrity of the content of the paper

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