An Efficient Diagnosis System for Parkinson’s Disease Using Kernel-Based Extreme Learning Machine with Subtractive Clustering Features Weighting Approach
المؤلفون المشاركون
Chen, Hui-ling
Ma, Chao
Ouyang, Jihong
Zhao, Xue-Hua
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2014، العدد 2014 (31 ديسمبر/كانون الأول 2014)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2014-11-17
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1034676
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر