عنوان انگلیسی مقاله:
Using supervised learning machine algorithm to identify future fallers based on gait patterns: A two-year longitudinal study
ترجمه فارسی عنوان مقاله:
استفاده از الگوریتم دستگاه یادگیری نظارت شده برای شناسایی متولدین آینده بر اساس الگوهای راه رفتن: یک مطالعه دو ساله طولی
Sciencedirect - Elsevier - Experimental Gerontology, Journal Pre-proof, 110730: doi:10:1016/j:exger:2019:110730
Sophie Gillain, Mohamed Boutaayamou, Cedric Schwartz, Olivier Brüls, Olivier Bruyère, Jean-Louis Croisier, Eric Salmon, Jean-Yves Reginster, Gaëtan Garraux, Jean Petermans
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 more than 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
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.
Keywords: Supervise Machine Learning Algorithm | Classification | Fall risk | Prospective | Older adults