XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes

Abstract This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63–89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inerti...

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Autores principales:	Byungjoo Noh, Changhong Youm, Eunkyoung Goh, Myeounggon Lee, Hwayoung Park, Hyojeong Jeon, Oh Yoen Kim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/17d7e71570154b499afc0ec772378862
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spelling	oai:doaj.org-article:17d7e71570154b499afc0ec7723788622021-12-02T17:38:27ZXGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes10.1038/s41598-021-91797-w2045-2322https://doaj.org/article/17d7e71570154b499afc0ec7723788622021-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-91797-whttps://doaj.org/toc/2045-2322Abstract This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63–89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inertial measurement unit sensors embedded in the shoe-type data loggers on both outsoles. A metric was defined to classify the fall risks, determined based on a set of questions determining the history of falls and fear of falls. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. Moreover, the definition of the fall levels was classified into high- and low-risk groups. At all speeds, three gait features were identified with the XGBoost (stride length, walking speed, and stance phase) that accurately classified the fall risk levels. The model accuracy in classifying fall risk levels ranged between 67–70% with 43–53% sensitivity and 77–84% specificity. Thus, we identified the optimal gait features for accurate fall risk level classification in older adults. The XGBoost model could inspire future works on fall prevention and the fall-risk assessment potential through the gait analysis of older adults.Byungjoo NohChanghong YoumEunkyoung GohMyeounggon LeeHwayoung ParkHyojeong JeonOh Yoen KimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Byungjoo Noh Changhong Youm Eunkyoung Goh Myeounggon Lee Hwayoung Park Hyojeong Jeon Oh Yoen Kim XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
description	Abstract This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63–89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inertial measurement unit sensors embedded in the shoe-type data loggers on both outsoles. A metric was defined to classify the fall risks, determined based on a set of questions determining the history of falls and fear of falls. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. Moreover, the definition of the fall levels was classified into high- and low-risk groups. At all speeds, three gait features were identified with the XGBoost (stride length, walking speed, and stance phase) that accurately classified the fall risk levels. The model accuracy in classifying fall risk levels ranged between 67–70% with 43–53% sensitivity and 77–84% specificity. Thus, we identified the optimal gait features for accurate fall risk level classification in older adults. The XGBoost model could inspire future works on fall prevention and the fall-risk assessment potential through the gait analysis of older adults.
format	article
author	Byungjoo Noh Changhong Youm Eunkyoung Goh Myeounggon Lee Hwayoung Park Hyojeong Jeon Oh Yoen Kim
author_facet	Byungjoo Noh Changhong Youm Eunkyoung Goh Myeounggon Lee Hwayoung Park Hyojeong Jeon Oh Yoen Kim
author_sort	Byungjoo Noh
title	XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
title_short	XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
title_full	XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
title_fullStr	XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
title_full_unstemmed	XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes
title_sort	xgboost based machine learning approach to predict the risk of fall in older adults using gait outcomes
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/17d7e71570154b499afc0ec772378862
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XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes

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