title: An efficient diagnosis system for Parkinson's disease using kernel-based extreme learning machine with subtractive clustering features weighting approach.
abstract: A novel hybrid method named SCFW-KELM, which integrates effective subtractive clustering features weighting and a fast classifier kernel-based extreme learning machine (KELM), has been introduced for the diagnosis of PD. In the proposed method, SCFW is used as a data preprocessing tool, which aims at decreasing the variance in features of the PD dataset, in order to further improve the diagnostic accuracy of the KELM classifier. The impact of the type of kernel functions on the performance of KELM has been investigated in detail. The efficiency and effectiveness of the proposed method have been rigorously evaluated against the PD dataset in terms of classification accuracy, sensitivity, specificity, area under the receiver operating characteristic (ROC) curve (AUC), f-measure, and kappa statistics value. Experimental results have demonstrated that the proposed SCFW-KELM significantly outperforms SVM-based, KNN-based, and ELM-based approaches and other methods in the literature and achieved highest classification results reported so far via 10-fold cross validation scheme, with the classification accuracy of 99.49%, the sensitivity of 100%, the specificity of 99.39%, AUC of 99.69%, the f-measure value of 0.9964, and kappa value of 0.9867. Promisingly, the proposed method might serve as a new candidate of powerful methods for the diagnosis of PD with excellent performance.
authors.affiliation: College of Computer Science and Technology, Jilin University, No. 2699, QianJin Road, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Chao
authors.initials: C
authors.lastname: Ma
authors.affiliation: College of Computer Science and Technology, Jilin University, No. 2699, QianJin Road, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Jihong
authors.initials: J
authors.lastname: Ouyang
authors.affiliation: College of Physics and Electronic Information, Wenzhou University, Wenzhou 325035, China.
authors.forename: Hui-Ling
authors.initials: HL
authors.lastname: Chen
authors.affiliation: College of Computer Science and Technology, Jilin University, No. 2699, QianJin Road, Changchun 130012, China ; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
authors.forename: Xue-Hua
authors.initials: XH
authors.lastname: Zhao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software