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.
Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Acknowledgements
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
This work was partly supported by grants from the National Natural Science Foundation of China (No. 61906181 and 72001032), and partly by the Strategic Partner Acceleration Award (80761- AU201) under the S\(\hat{\text {e}}\)r Cymru II programme, UK.
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Chen, T. et al. (2022). Assessing Significance of Cognitive Assessments for Diagnosing Alzheimer’s Disease with Fuzzy-Rough Feature Selection. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_40
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