Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer's Disease

Thapa, S; Singh, P; Jain, DK; Bharill, N; Gupta, A; Prasad, M

Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer's Disease

Thapa, S Singh, P Jain, DK Bharill, N Gupta, A Prasad, M

Permalink

Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-8
Issue Date:: 2020-07-01

Closed Access

	Filename	Description	Size
	09207359.pdf	Published version	832.62 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Thapa, S
dc.contributor.author	Singh, P
dc.contributor.author	Jain, DK
dc.contributor.author	Bharill, N
dc.contributor.author	Gupta, A
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.date	2020-07-19
dc.date.accessioned	2021-03-16T01:16:08Z
dc.date.available	2021-03-16T01:16:08Z
dc.date.issued	2020-07-01
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2020, 00, pp. 1-8
dc.identifier.isbn	9781728169262
dc.identifier.uri	http://hdl.handle.net/10453/147225
dc.description.abstract	© 2020 IEEE. Alzheimer's disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	2020 International Joint Conference on Neural Networks (IJCNN)
dc.relation.isbasedon	10.1109/IJCNN48605.2020.9207359
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Data-Driven Approach based on Feature Selection Technique for Early Diagnosis of Alzheimer's Disease
dc.type	Conference Proceeding
utslib.citation.volume	00
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/Strength - AAII - Australian Artificial Intelligence Institute
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	2021-03-16T01:16:06Z
pubs.finish-date	2020-07-24
pubs.publication-status	Published
pubs.start-date	2020-07-19
pubs.volume	00

Abstract:

© 2020 IEEE. Alzheimer's disease (AD) is a neurodegenerative disorder resulting in memory loss and cognitive decline caused due to the death of brain cells. It is the most common form of dementia and accounts for 60-80% of all dementia cases. There is no single test for diagnosis of AD, the doctors rely on medical history, neuropsychological assessments, computed tomography (CT) or magnetic resonance imaging (MRI) scan of the brain, etc. to confirm a diagnosis. In terms of the treatment, currently, there is neither a cure nor any way to slow the progression of AD. However, for people with mild or moderate stages of this disease, there are some medications available to temporarily reduce symptoms and help to improve quality of life. Hence, early diagnosis of AD is extremely crucial for overall better management of the disease. The researches have shown some relation between neuropsychological scores and atrophies of the brain. This can be leveraged for the early diagnosis of AD. This paper makes use of feature selection techniques to extract the most important features in the diagnosis of AD. This paper demonstrates the need to combine neuropsychological scores like mini-mental state examination (MMSE) with MRI features to provide better decisional space for early diagnosis of AD. Through the experiments, including MMSE along with other features are found to improve the classification of AD, significantly.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/147225