An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach

Joint Authors

Chen, Hui-ling
Ma, Chao
Ouyang, Jihong
Zhao, Xue-Hua

Source

Computational and Mathematical Methods in Medicine

Issue

Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-14, 14 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2014-11-17

Country of Publication

Egypt

No. of Pages

14

Main Subjects

Medicine

Abstract EN

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.

American Psychological Association (APA)

Ma, Chao& Ouyang, Jihong& Chen, Hui-ling& Zhao, Xue-Hua. 2014. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach. Computational and Mathematical Methods in Medicine،Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1034676

Modern Language Association (MLA)

Ma, Chao…[et al.]. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach. Computational and Mathematical Methods in Medicine No. 2014 (2014), pp.1-14.
https://search.emarefa.net/detail/BIM-1034676

American Medical Association (AMA)

Ma, Chao& Ouyang, Jihong& Chen, Hui-ling& Zhao, Xue-Hua. An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach. Computational and Mathematical Methods in Medicine. 2014. Vol. 2014, no. 2014, pp.1-14.
https://search.emarefa.net/detail/BIM-1034676

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1034676