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Broad Learning with Attribute Selection for Rheumatoid Arthritis
Jie Yang1,2; Shigao Huang3; Rui Tang4; Quanyi Hu1; Kun Lan1; Han Wang5,6; Qi Zhao3; Simon Fong1
2020-12-14
Conference Name2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Source PublicationIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume2020-October
Pages552-558
Conference Date11-14 Oct. 2020
Conference PlaceToronto, ON, Canada
Abstract

Rheumatoid arthritis (RA) patients have osteoarticular deformation in the early stage, and suffer worse from joint deformity and even loss of function in the later stage. Accurate evaluation of the patient's physical condition is of importance as it would significantly help to decide appropriate care, medications or medical interventions needed. Thus, a fast and efficient risk factor selection algorithm demonstrates a clinical significance for the more precise diagnosis, and an accurate prediction model will hopefully be able to improve current treatment. In this paper, we designed a novel and universal architecture, broad learning attribute selection system (BLAS), to deal with the risk factor diagnosis and disease performance prediction on RA patients. The attribute selection based on rough set and entropy can identify significant risk factors affecting RA and broad learning possesses the ability of randomly generating nodes to investigate the desired connection weights simultaneously without the need for deep architecture. Experiments on clinical RA patients' dataset demonstrated that our proposed BLAS model achieved the highest average accuracy of 99.67% with mean absolute error of 0.32%, compared with the state-of-the-art methods. The results proved the robust classification ability of BLAS in RA risk factors assessment and prediction.

KeywordBroad Learning Attribute Selection Disease Pre-diction Rheumatoid Arthritis
DOI10.1109/SMC42975.2020.9283396
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science
WOS SubjectComputer Science, Cybernetics ; Computer Science, Information Systems
WOS IDWOS:000687430600086
Scopus ID2-s2.0-85098847719
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Faculty of Health Sciences
Corresponding AuthorJie Yang
Affiliation1.Depart. of Computer and Information Science, University of Macau, Macao, China
2.Chongqing Industry&Trade Polytechnic, Chongqing, China
3.Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, China
4.Dept. of Management Science and Info. System, Kunming University of Science and Technology, Kunming, China
5.Institute of Data Science, City Univerity of Macau, Macao, China
6.Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Jie Yang,Shigao Huang,Rui Tang,et al. Broad Learning with Attribute Selection for Rheumatoid Arthritis[C],2020:552-558.
APA Jie Yang,Shigao Huang,Rui Tang,Quanyi Hu,Kun Lan,Han Wang,Qi Zhao,&Simon Fong.(2020).Broad Learning with Attribute Selection for Rheumatoid Arthritis.IEEE Transactions on Systems, Man, and Cybernetics: Systems,2020-October,552-558.
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