Fuzzy PCA and Support Vector Machines for Breast Cancer Classification

  • Authors

    • Mohamad Faiz Dzulkalnine
    • Roselina Sallehuddin
    • Yusliza Yusoff
    • Nor Haizan Mohamed Radzi
    • Noorfa Haszlinna Mustaffa
    2018-07-04
    https://doi.org/10.14419/ijet.v7i3.7.16210
  • Feature Selection, classification, accuracy, Fuzzy PCA, SVM.

  • 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.

     

     

  • References

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  • How to Cite

    Faiz Dzulkalnine, M., Sallehuddin, R., Yusoff, Y., Haizan Mohamed Radzi, N., & Haszlinna Mustaffa, N. (2018). Fuzzy PCA and Support Vector Machines for Breast Cancer Classification. International Journal of Engineering & Technology, 7(3.7), 62-64. https://doi.org/10.14419/ijet.v7i3.7.16210