Supervised classification of geriatric anxiety

Jae Kyeong Sim; Hyungtai Kim; Geon Ha Kim; Mun Taek Choi

doi:10.1145/3321454.3321457

Supervised classification of geriatric anxiety

Jae Kyeong Sim, Hyungtai Kim, Geon Ha Kim, Mun Taek Choi

Department of Neurology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly. As a result, we showed that classification is possible without using all standard GAI questionnaires.

Original language	English
Title of host publication	ACM International Conference Proceeding Series
Publisher	Association for Computing Machinery
Pages	73-77
Number of pages	5
ISBN (Print)	9781450366335
DOIs	https://doi.org/10.1145/3321454.3321457
State	Published - 2019
Event	4th International Conference on Intelligent Information Technology, ICIIT 2019 - Da Nang, Viet Nam Duration: 20 Feb 2019 → 23 Feb 2019

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F147957

Conference

Conference	4th International Conference on Intelligent Information Technology, ICIIT 2019
Country/Territory	Viet Nam
City	Da Nang
Period	20/02/19 → 23/02/19

Bibliographical note

Funding Information:
This work was supported by the Technology Innovation Program (10063300) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea)

Publisher Copyright:
© 2019 Association for Computing Machinery.

Keywords

Feature selection
GAI
Geriatric anxiety
Supervised classification

Access to Document

10.1145/3321454.3321457

Cite this

@inproceedings{b0303445b469482a8507494d4386136e,

title = "Supervised classification of geriatric anxiety",

abstract = "Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly. As a result, we showed that classification is possible without using all standard GAI questionnaires.",

keywords = "Feature selection, GAI, Geriatric anxiety, Supervised classification",

author = "Sim, {Jae Kyeong} and Hyungtai Kim and Kim, {Geon Ha} and Choi, {Mun Taek}",

note = "Funding Information: This work was supported by the Technology Innovation Program (10063300) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 4th International Conference on Intelligent Information Technology, ICIIT 2019 ; Conference date: 20-02-2019 Through 23-02-2019",

year = "2019",

doi = "10.1145/3321454.3321457",

language = "English",

isbn = "9781450366335",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "73--77",

booktitle = "ACM International Conference Proceeding Series",

}

Sim, JK, Kim, H, Kim, GH & Choi, MT 2019, Supervised classification of geriatric anxiety. in ACM International Conference Proceeding Series. ACM International Conference Proceeding Series, vol. Part F147957, Association for Computing Machinery, pp. 73-77, 4th International Conference on Intelligent Information Technology, ICIIT 2019, Da Nang, Viet Nam, 20/02/19. https://doi.org/10.1145/3321454.3321457

Supervised classification of geriatric anxiety. / Sim, Jae Kyeong; Kim, Hyungtai; Kim, Geon Ha et al.
ACM International Conference Proceeding Series. Association for Computing Machinery, 2019. p. 73-77 (ACM International Conference Proceeding Series; Vol. Part F147957).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kim, Hyungtai

AU - Kim, Geon Ha

AU - Choi, Mun Taek

N1 - Funding Information: This work was supported by the Technology Innovation Program (10063300) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea) Publisher Copyright: © 2019 Association for Computing Machinery.

PY - 2019

Y1 - 2019

N2 - Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly. As a result, we showed that classification is possible without using all standard GAI questionnaires.

AB - Anxiety is a common symptom in elderly people and is associated with dementia. In this study, we apply the machine learning methods to classify anxiety patients based on GAI. We confirm the possibility of reducing the number of GAI questionnaires, which is to make GAI testing easier for the elderly. As a result, we showed that classification is possible without using all standard GAI questionnaires.

KW - Feature selection

KW - GAI

KW - Geriatric anxiety

KW - Supervised classification

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U2 - 10.1145/3321454.3321457

DO - 10.1145/3321454.3321457

M3 - Conference contribution

AN - SCOPUS:85066038788

SN - 9781450366335

T3 - ACM International Conference Proceeding Series

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BT - ACM International Conference Proceeding Series

PB - Association for Computing Machinery

T2 - 4th International Conference on Intelligent Information Technology, ICIIT 2019

Y2 - 20 February 2019 through 23 February 2019

ER -

Supervised classification of geriatric anxiety

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

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Cite this