An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease

Neurocomputing

Volumn 184, Issue , 2016, Pages 131-144

  Chen, Hui Ling ^a,b   Wang, Gang ^b   Ma, Chao ^c   Cai, Zhen Nao ^a,d   Liu, Wen Bin ^a   Wang, Su Jing ^b,e  

^a WENZHOU UNIVERSITY   (China)

^b JILIN UNIVERSITY   (China)

^c SHENZHEN INSTITUTE OF INFORMATION TECHNOLOGY   (China)

^d NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^e INSTITUTE OF PSYCHOLOGY   (China)

Author keywords

Feature selection; Kernel extreme learning machine; Medical diagnosis; Parkinson's disease

Indexed keywords

CLASSIFICATION (OF INFORMATION); DIAGNOSIS; FEATURE EXTRACTION; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES;

10-FOLD CROSS-VALIDATION; ACTIVATION FUNCTIONS; CLASSIFICATION ACCURACY; CLASSIFICATION MODELS; EXTREME LEARNING MACHINE; NUMBER OF HIDDEN NEURONS; PARKINSON'S DISEASE; RECEIVER OPERATING CHARACTERISTICS;

COMPUTER AIDED DIAGNOSIS;

ARTICLE; AUTOMATED PATTERN RECOGNITION; CLASSIFIER; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; EARLY DIAGNOSIS; EXTREME LEARNING MACHINE; HUMAN; INTERMETHOD COMPARISON; KERNEL EXTREME LEARNING MACHINE; MACHINE LEARNING; PARKINSON DISEASE; PRIORITY JOURNAL; RADIAL BASED FUNCTION; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 84950341558     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.07.138     Document Type: Article

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