In Experimental gerontology
INTRODUCTION : Given their major health consequences in the elderly, identifying people at risk of fall is a major challenge faced by clinicians. A lot of studies have confirmed the relationships between gait parameters and falls incidence. However, accurate tools to predict individual risk among independent older adults without a history of falls are lacking.
OBJECTIVE : This study aimed to apply a supervised learning algorithm to a data set recorded in a two-year longitudinal study, in order to build a classification tree that could discern subsequent fallers based on their gait patterns.
METHODS : A total of 105 adults aged >65 years, living independently at home and without a recent fall history were included in a two-year longitudinal study. All underwent physical and functional assessment. Gait speed, stride length, frequency, symmetry and regularity, and minimum toe clearance were recorded in comfortable, fast and dual task walking conditions in a standardized laboratory environment. Fall events were recorded using personal falls diaries. A supervised machine learning algorithm (J48) has been applied to the data recorded at inclusion in order to obtain a classification tree able to identify future fallers.
RESULTS : Based on fall information from 96 volunteers, a classification tree correctly identifying 80% of future fallers based on gait patterns, gender, and stiffness, was obtained, with accuracy of 84%, sensitivity of 80%, specificity of 87%, a positive predictive value of 78%, and a negative predictive value of 88%.
DISCUSSION : While the performances of the classification tree warrant further confirmation, it is the first predictive tool based on gait parameters that are identified (not clustered) allowing its use by other research teams.
CONCLUSION : This original longitudinal pilot study using a supervised machine learning algorithm, shows that gait parameters and clinical data can be used to identify future fallers among independent older adults.
Gillain Sophie, Boutaayamou Mohamed, Schwartz Cedric, Brüls Olivier, Bruyère Olivier, Croisier Jean-Louis, Salmon Eric, Reginster Jean-Yves, Garraux Gaëtan, Petermans Jean
Classification, Fall risk, Older adults, Prospective, Supervise machine learning algorithm