Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.

This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's...

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Autores principales:	Luay Fraiwan, Omnia Hassanin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/94cb636804864c36bbf2e3df4b043f00
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spelling	oai:doaj.org-article:94cb636804864c36bbf2e3df4b043f002021-11-25T06:23:36ZComputer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.1932-620310.1371/journal.pone.0252380https://doaj.org/article/94cb636804864c36bbf2e3df4b043f002021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252380https://doaj.org/toc/1932-6203This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.Luay FraiwanOmnia HassaninPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252380 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Luay Fraiwan Omnia Hassanin Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
description	This study proposes a reliable computer-aided framework to identify gait fluctuations associated with a wide range of degenerative neuromuscular disease (DNDs) and health conditions. Investigated DNDs included amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), and Huntington's disease (HD). We further performed a statistical and classification comparison elucidating the discriminative capability of different gait signals, including vertical ground reaction force (VGRF), stride duration, stance duration, and swing duration. Feature representation of these gait signals was based on statistical amplitude quantification using the root mean square (RMS), variance, kurtosis, and skewness metrics. We investigated various decision tree (DT) based ensemble methods such as bagging, adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and random subspace to tackle the challenge of multi-class classification. Experimental results showed that AdaBoost ensembling provided a 6.49%, 0.78%, 2.31%, and 2.72% prediction rate improvement for the VGRF, stride, stance, and swing signals, respectively. The proposed approach achieved the highest classification accuracy of 99.17%, sensitivity of 98.23%, and specificity of 99.43%, using the VGRF-based features and the adaptive boosting classification model. This work demonstrates the effective capability of using simple gait fluctuation analysis and machine learning approaches to detect DNDs. Computer-aided analysis of gait fluctuations provides a promising advent to enhance clinical diagnosis of DNDs.
format	article
author	Luay Fraiwan Omnia Hassanin
author_facet	Luay Fraiwan Omnia Hassanin
author_sort	Luay Fraiwan
title	Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
title_short	Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
title_full	Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
title_fullStr	Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
title_full_unstemmed	Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
title_sort	computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/94cb636804864c36bbf2e3df4b043f00
work_keys_str_mv	AT luayfraiwan computeraidedidentificationofdegenerativeneuromusculardiseasesbasedongaitdynamicsandensembledecisiontreeclassifiers AT omniahassanin computeraidedidentificationofdegenerativeneuromusculardiseasesbasedongaitdynamicsandensembledecisiontreeclassifiers
_version_	1718413814325575680

Computer-aided identification of degenerative neuromuscular diseases based on gait dynamics and ensemble decision tree classifiers.

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