Fuzzy PCA and Support Vector Machines for Breast Cancer Classification

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Breast cancer is the leading cause of death among women in the world and early detection can increase the chance of survival for the patients. However, expert system and machine learning diagnosis are burdened with the presence of irrelevant data and noise which can reduce the accuracy of prediction and increase computational time. In this paper, Fuzzy Principle Component Analysis (FPCA) and Support Vector Machines (SVM) are proposed for the classification of breast cancer dataset. Experimental results on public breast cancer dataset show that the proposed method FPCA-SVM outperformed the benchmark models in terms of accuracy, specificity, and sensitivity and AUC value. The proposed model can assist doctors and medical practitioners for an early detection of breast cancer.

     

     

  • Keywords


    Feature Selection, classification, accuracy, Fuzzy PCA, SVM.

  • References


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      [4] Tanatavikorn, H. and Y. Yamashita (2016). "Fuzzy Treatment Method for Outlier Detection in Process Data." Journal of Chemical Engineering of Japan 49(9): 864-873.

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Article ID: 16210
 
DOI: 10.14419/ijet.v7i3.7.16210




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