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 proceedingConference contributionpeer-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 languageEnglish
Title of host publication20th UK Workshop on Computational Intelligence (UKCI 2021)
Number of pages12
Publication statusAccepted/In press - 13 Jul 2021
Event20th UK Workshop on Computational Intelligence - Aberystwyth University, Aberystwyth, United Kingdom
Duration: 8 Sep 202110 Sep 2021
https://ukci2021.dcs.aber.ac.uk/

Conference

Conference20th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2021
CountryUnited Kingdom
CityAberystwyth
Period8/09/2110/09/21
Internet address

Fingerprint

Dive into the research topics of 'Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer's Disease with Fuzzy-Rough Feature Selection'. Together they form a unique fingerprint.

Cite this