Deep Learning-Based Classification of Neurodegenerative Diseases Using Gait Dataset: A Comparative Study

Zhou, Z; Kanwal, A; Chaturvedi, K; Raza, R; Prakash, S; Jan, T; Prasad, M

Deep Learning-Based Classification of Neurodegenerative Diseases Using Gait Dataset: A Comparative Study

Zhou, Z

Kanwal, A Chaturvedi, K

Raza, R Prakash, S Jan, T Prasad, M

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: ACM International Conference Proceeding Series, 2023, pp. 59-64
Issue Date:: 2023-07-21

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Field	Value	Language
dc.contributor.author	Zhou, Z https://orcid.org/0009-0007-7788-6339
dc.contributor.author	Kanwal, A
dc.contributor.author	Chaturvedi, K https://orcid.org/0000-0001-9940-1565
dc.contributor.author	Raza, R
dc.contributor.author	Prakash, S
dc.contributor.author	Jan, T
dc.contributor.author	Prasad, M
dc.date.accessioned	2023-12-19T14:29:57Z
dc.date.available	2023-12-19T14:29:57Z
dc.date.issued	2023-07-21
dc.identifier.citation	ACM International Conference Proceeding Series, 2023, pp. 59-64
dc.identifier.isbn	9798400707742
dc.identifier.uri	http://hdl.handle.net/10453/173877
dc.description.abstract	Neuro-degenerative diseases pose a significant health concern for human society, especially among the elderly population. Given their high prevalence and the limited availability of clinical expertise and services, there is an urgent requirement for the integration of artificial intelligence (AI) systems to support healthcare professionals in addressing this issue. This paper presents a comparative analysis of seven deep learning architectures for neuro-degenerative disease classification by using a gait dynamics dataset. The models used in the analysis include LSTM, GRU, InceptionTime, ResNet, FCN, TST, and PatchTST. The models are extensively evaluated for different classification tasks. The findings of the study suggest that deep learning techniques have the potential to diagnose and classify different neurodegenerative diseases effectively. It can be inferred that ResNet exhibits superior performance compared to other models in tasks involving the classification of healthy controls (HC) and class of individuals with all neuro-degenerative diseases (NDDs), and classification of healthy controls (HC) from individuals with Parkinson's disease (PD). TST demonstrates the highest level of performance among all models in tasks involving distinguishing between Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC), as well as Huntington's disease (HD) and healthy controls (HC).
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	ACM International Conference Proceeding Series
dc.relation.ispartof	Proceedings of the 2023 International Conference on Robotics, Control and Vision Engineering
dc.relation.isbasedon	10.1145/3608143.3608154
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Deep Learning-Based Classification of Neurodegenerative Diseases Using Gait Dataset: A Comparative Study
dc.type	Conference Proceeding
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-12-19T14:29:56Z
pubs.publication-status	Published

Abstract:

Neuro-degenerative diseases pose a significant health concern for human society, especially among the elderly population. Given their high prevalence and the limited availability of clinical expertise and services, there is an urgent requirement for the integration of artificial intelligence (AI) systems to support healthcare professionals in addressing this issue. This paper presents a comparative analysis of seven deep learning architectures for neuro-degenerative disease classification by using a gait dynamics dataset. The models used in the analysis include LSTM, GRU, InceptionTime, ResNet, FCN, TST, and PatchTST. The models are extensively evaluated for different classification tasks. The findings of the study suggest that deep learning techniques have the potential to diagnose and classify different neurodegenerative diseases effectively. It can be inferred that ResNet exhibits superior performance compared to other models in tasks involving the classification of healthy controls (HC) and class of individuals with all neuro-degenerative diseases (NDDs), and classification of healthy controls (HC) from individuals with Parkinson's disease (PD). TST demonstrates the highest level of performance among all models in tasks involving distinguishing between Amyotrophic Lateral Sclerosis (ALS) and healthy controls (HC), as well as Huntington's disease (HD) and healthy controls (HC).

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http://hdl.handle.net/10453/173877