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An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease
Chen, Hui-Ling1; Wang, Gang2; Ma, Chao3; Cai, Zhen-Nao1,4; Liu, Wen-Bin1; Wang, Su-Jing5,6
2016-04-05
Source PublicationNEUROCOMPUTING
ISSN0925-2312
SubtypeArticle
Volume184Issue:0Pages:131-144
AbstractIn this paper, we explore the potential of extreme learning machine (ELM) and kernel ELM (KELM) for early diagnosis of Parkinson's disease (PD). In the proposed method, the key parameters including the number of hidden neuron and type of activation function in ELM, and the constant parameter C and kernel parameter gamma in KELM are investigated in detail. With the obtained optimal parameters, ELM and KELM manage to train the optimal predictive models for PD diagnosis. In order to further improve the performance of ELM and KELM models, feature selection techniques are implemented prior to the construction of the classification models. The effectiveness of the proposed method has been rigorously evaluated against the PD data set in terms of classification accuracy, sensitivity, specificity and the area under the ROC (receiver operating characteristic) curve (AUC). Compared to the existing methods in previous studies, the proposed method has achieved very promising classification accuracy via 10-fold cross-validation (CV) analysis, with the highest accuracy of 96.47% and average accuracy of 95.97% over 10 runs of 10-fold CV. (C) 2015 Elsevier B.V. All rights reserved.
KeywordKernel Extreme Learning Machine Feature Selection Medical Diagnosis Parkinson's Disease
DOI10.1016/j.neucom.2015.07.138
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61303113 ; Zhejiang Provincial Natural Science Foundation of China(R1110261 ; Science and Technology Plan Project of Wenzhou, China(G20140048) ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education ; Open Projects Program of National Laboratory of Pattern Recognition(201306295) ; Beijing Natural Science Foundation(4152055) ; 61379095 ; LY14F020035 ; 61272018 ; LQ13G010007 ; 61402337 ; LQ13F020011) ; 61572367)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000374364300014
WOS HeadingsScience & Technology ; Technology
WOS KeywordFEEDFORWARD NETWORKS ; CLASSIFICATION ; SPEECH ; PERFORMANCE ; ALGORITHMS ; RELEVANCE ; ACCURACY ; ENSEMBLE ; NUMBER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/19987
Collection脑与认知科学国家重点实验室
Affiliation1.Wenzhou Univ, Coll Phys & Elect Informat, Wenzhou 325035, Zhejiang, Peoples R China
2.Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China
3.Shenzhen Inst Informat Technol, Sch Digital Media, Shenzhen 518172, Peoples R China
4.Northwestern Polytech Univ, Sch Comp Sci & Engn, Xian 710072, Peoples R China
5.Chinese Acad Sci, Inst Psychol, State Key Lab Brain & Cognit Sci, Beijing 100101, Peoples R China
6.Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
Recommended Citation
GB/T 7714
Chen, Hui-Ling,Wang, Gang,Ma, Chao,et al. An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease[J]. NEUROCOMPUTING,2016,184(0):131-144.
APA Chen, Hui-Ling,Wang, Gang,Ma, Chao,Cai, Zhen-Nao,Liu, Wen-Bin,&Wang, Su-Jing.(2016).An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease.NEUROCOMPUTING,184(0),131-144.
MLA Chen, Hui-Ling,et al."An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease".NEUROCOMPUTING 184.0(2016):131-144.
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