A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer's Disease

Adhikari, S; Thapa, S; Singh, P; Huo, H; Bharathy, G; Prasad, M

A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer's Disease

Adhikari, S Thapa, S Singh, P Huo, H

Bharathy, G

Prasad, M

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2021, 2021-July, pp. 1-8
Issue Date:: 2021-07-18

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Field	Value	Language
dc.contributor.author	Adhikari, S
dc.contributor.author	Thapa, S
dc.contributor.author	Singh, P
dc.contributor.author	Huo, H https://orcid.org/0000-0003-2440-714X
dc.contributor.author	Bharathy, G https://orcid.org/0000-0001-8384-9509
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253
dc.date	2021-06-18
dc.date.accessioned	2021-11-17T21:50:25Z
dc.date.available	2021-11-17T21:50:25Z
dc.date.issued	2021-07-18
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2021, 2021-July, pp. 1-8
dc.identifier.isbn	9780738133669
dc.identifier.issn	2161-4393
dc.identifier.issn	2161-4407
dc.identifier.uri	http://hdl.handle.net/10453/151675
dc.description.abstract	Alzheimer's Disease (AD) is one of the most common forms of neuropsychological disorder in elderly people. It is a slow progressive disease affecting the brain cells. This affects the cognitive abilities of people and their daily activities. During the course of the disease, memory gets brutally affected too. Working as well as long-term declarative memory deteriorates in AD patients. Due to this deterioration of the memory, AD patients tend to show a decline in their communicative skills as well. This decline is reflected in their speech. AD patients usually have poor grammar along with very low coherent ideas. Also, they tend to repeat the words very often and hence become unclear on the message they are trying to convey. As the disease progresses, the speech is completely impaired, and the patients are left to sing or utter words that are totally out of context. Stopwords are the words that are most commonly used in language and it is often hypothesized that AD patients use them much often as compared to Control Normal (CN) subjects. It is seen that due to the degeneration of brain cells in AD patients, they have a tendency to use a lot of stopwords to fill their perplexities in their statements. In this paper, the usefulness of the stopwords in capturing the linguistic information of the patients suffering from AD are discussed. Learning algorithms are evaluated by including stopwords and dropping stopwords at preprocessing to draw comparisons.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks
dc.relation.ispartof	International Joint Conference on Neural Networks
dc.relation.isbasedon	10.1109/IJCNN52387.2021.9534449
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Comparative Study of Machine Learning and NLP Techniques for Uses of Stop Words by Patients in Diagnosis of Alzheimer's Disease
dc.type	Conference Proceeding
utslib.citation.volume	2021-July
utslib.location.activity	Shenzhen, China
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 Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-11-17T21:50:24Z
pubs.finish-date	2021-06-22
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-06-18
pubs.volume	2021-July
dc.location	Piscataway, USA

Abstract:

Alzheimer's Disease (AD) is one of the most common forms of neuropsychological disorder in elderly people. It is a slow progressive disease affecting the brain cells. This affects the cognitive abilities of people and their daily activities. During the course of the disease, memory gets brutally affected too. Working as well as long-term declarative memory deteriorates in AD patients. Due to this deterioration of the memory, AD patients tend to show a decline in their communicative skills as well. This decline is reflected in their speech. AD patients usually have poor grammar along with very low coherent ideas. Also, they tend to repeat the words very often and hence become unclear on the message they are trying to convey. As the disease progresses, the speech is completely impaired, and the patients are left to sing or utter words that are totally out of context. Stopwords are the words that are most commonly used in language and it is often hypothesized that AD patients use them much often as compared to Control Normal (CN) subjects. It is seen that due to the degeneration of brain cells in AD patients, they have a tendency to use a lot of stopwords to fill their perplexities in their statements. In this paper, the usefulness of the stopwords in capturing the linguistic information of the patients suffering from AD are discussed. Learning algorithms are evaluated by including stopwords and dropping stopwords at preprocessing to draw comparisons.

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http://hdl.handle.net/10453/151675