Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection

Tianhua Chen; Changjing Shang; Pan  Su; Yinghua Shen; Mufti Mahmud; Raymond Moodley; Grigoris Antoniou; Qiang Shen

doi:10.1007/978-3-030-87094-2_40

Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection

Tianhua Chen, Changjing Shang, Pan Su, Yinghua Shen, Mufti Mahmud, Raymond Moodley, Grigoris Antoniou, Qiang Shen

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

Abstract

Research in dementia diagnosis typically involves a range of data modalities and also, the use of cognitive assessments, aiming at the development of approaches that are non-invasive, time-saving and economical. Given the existing diversity of prevalent cognitive assessment factors it is useful to assess and exploit the effectiveness of such cognitive features, while working towards the establishment of a methodology for making informed choice of such factors in practical use. As an initial approach, this paper employs the powerful Fuzzy-Rough Feature Selection (FRFS) technique to support such an analysis, by varying the underlying similarity functions and search strategies employed by FRFS. Evaluated on a benchmark from the renowned Alzheimer’s Disease Neuroimaging Initiative repository, experimental results demonstrate the significance and predictive capabilities of different cognitive assessments in working with a variety of popular classifiers.

Original language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK
Editors	Thomas Jansen, Richard Jensen, Neil Mac Parthaláin, Chih-Min Lin
Place of Publication	Cham
Publisher	Springer, Cham
Pages	450-462
Number of pages	13
Volume	AISC 1409
Edition	1st
ISBN (Electronic)	9783030870942
ISBN (Print)	9783030870935
DOIs	https://doi.org/10.1007/978-3-030-87094-2_40
Publication status	Published - 18 Nov 2021
Event	20th UK Workshop on Computational Intelligence - Aberystwyth University, Aberystwyth, United Kingdom Duration: 8 Sep 2021 → 10 Sep 2021 https://ukci2021.dcs.aber.ac.uk/

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Number	1409
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	20th UK Workshop on Computational Intelligence
Abbreviated title	UKCI 2021
Country/Territory	United Kingdom
City	Aberystwyth
Period	8/09/21 → 10/09/21
Internet address	https://ukci2021.dcs.aber.ac.uk/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-87094-2_40Licence: Unspecified

UKCI 2021 camera ready versionAccepted author manuscript, 274 KBLicence: Unspecified

Cite this

Chen, T., Shang, C., Su, P., Shen, Y., Mahmud, M., Moodley, R., Antoniou, G., & Shen, Q. (2021). Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection. In T. Jansen, R. Jensen, N. Mac Parthaláin, & C-M. Lin (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK (1st ed., Vol. AISC 1409, pp. 450-462). (Advances in Intelligent Systems and Computing; No. 1409). Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_40

Chen, Tianhua ; Shang, Changjing ; Su, Pan et al. / Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection. Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK. editor / Thomas Jansen ; Richard Jensen ; Neil Mac Parthaláin ; Chih-Min Lin. Vol. AISC 1409 1st. ed. Cham : Springer, Cham, 2021. pp. 450-462 (Advances in Intelligent Systems and Computing; 1409).

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title = "Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection",

abstract = "Research in dementia diagnosis typically involves a range of data modalities and also, the use of cognitive assessments, aiming at the development of approaches that are non-invasive, time-saving and economical. Given the existing diversity of prevalent cognitive assessment factors it is useful to assess and exploit the effectiveness of such cognitive features, while working towards the establishment of a methodology for making informed choice of such factors in practical use. As an initial approach, this paper employs the powerful Fuzzy-Rough Feature Selection (FRFS) technique to support such an analysis, by varying the underlying similarity functions and search strategies employed by FRFS. Evaluated on a benchmark from the renowned Alzheimer{\textquoteright}s Disease Neuroimaging Initiative repository, experimental results demonstrate the significance and predictive capabilities of different cognitive assessments in working with a variety of popular classifiers.",

keywords = "Dementia, Cognitive measure, Fuzzy-rough feature selection",

author = "Tianhua Chen and Changjing Shang and Pan Su and Yinghua Shen and Mufti Mahmud and Raymond Moodley and Grigoris Antoniou and Qiang Shen",

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booktitle = "Advances in Computational Intelligence Systems",

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Chen, T, Shang, C, Su, P, Shen, Y, Mahmud, M, Moodley, R, Antoniou, G & Shen, Q 2021, Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection. in T Jansen, R Jensen, N Mac Parthaláin & C-M Lin (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK. 1st edn, vol. AISC 1409, Advances in Intelligent Systems and Computing, no. 1409, Springer, Cham, Cham, pp. 450-462, 20th UK Workshop on Computational Intelligence, Aberystwyth, United Kingdom, 8/09/21. https://doi.org/10.1007/978-3-030-87094-2_40

Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection. / Chen, Tianhua; Shang, Changjing; Su, Pan et al.

Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK. ed. / Thomas Jansen; Richard Jensen; Neil Mac Parthaláin; Chih-Min Lin. Vol. AISC 1409 1st. ed. Cham : Springer, Cham, 2021. p. 450-462 (Advances in Intelligent Systems and Computing; No. 1409).

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

TY - GEN

T1 - Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection

AU - Chen, Tianhua

AU - Shang, Changjing

AU - Su, Pan

AU - Shen, Yinghua

AU - Mahmud, Mufti

AU - Moodley, Raymond

AU - Antoniou, Grigoris

AU - Shen, Qiang

PY - 2021/11/18

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N2 - Research in dementia diagnosis typically involves a range of data modalities and also, the use of cognitive assessments, aiming at the development of approaches that are non-invasive, time-saving and economical. Given the existing diversity of prevalent cognitive assessment factors it is useful to assess and exploit the effectiveness of such cognitive features, while working towards the establishment of a methodology for making informed choice of such factors in practical use. As an initial approach, this paper employs the powerful Fuzzy-Rough Feature Selection (FRFS) technique to support such an analysis, by varying the underlying similarity functions and search strategies employed by FRFS. Evaluated on a benchmark from the renowned Alzheimer’s Disease Neuroimaging Initiative repository, experimental results demonstrate the significance and predictive capabilities of different cognitive assessments in working with a variety of popular classifiers.

AB - Research in dementia diagnosis typically involves a range of data modalities and also, the use of cognitive assessments, aiming at the development of approaches that are non-invasive, time-saving and economical. Given the existing diversity of prevalent cognitive assessment factors it is useful to assess and exploit the effectiveness of such cognitive features, while working towards the establishment of a methodology for making informed choice of such factors in practical use. As an initial approach, this paper employs the powerful Fuzzy-Rough Feature Selection (FRFS) technique to support such an analysis, by varying the underlying similarity functions and search strategies employed by FRFS. Evaluated on a benchmark from the renowned Alzheimer’s Disease Neuroimaging Initiative repository, experimental results demonstrate the significance and predictive capabilities of different cognitive assessments in working with a variety of popular classifiers.

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VL - AISC 1409

T3 - Advances in Intelligent Systems and Computing

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BT - Advances in Computational Intelligence Systems

A2 - Jansen, Thomas

A2 - Jensen, Richard

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CY - Cham

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Y2 - 8 September 2021 through 10 September 2021

ER -

Chen T, Shang C, Su P, Shen Y, Mahmud M, Moodley R et al. Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection. In Jansen T, Jensen R, Mac Parthaláin N, Lin C-M, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, September 8-10, 2021, Aberystwyth, Wales, UK. 1st ed. Vol. AISC 1409. Cham: Springer, Cham. 2021. p. 450-462. (Advances in Intelligent Systems and Computing; 1409). Epub 2021 Nov 18. doi: 10.1007/978-3-030-87094-2_40

Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection

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