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

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.