A Support Vector Machine Approach to Breast Cancer Diagnosis and Prognosis

  • Elias Zafiropoulos
  • Ilias Maglogiannis
  • Ioannis Anagnostopoulos
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 204)

Abstract

In recent years, computational diagnostic tools and artificial intelligence techniques provide automated procedures for objective judgments by making use of quantitative measures and machine learning. The paper presents a Support Vector Machine (SVM) approach for the prognosis and diagnosis of breast cancer implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and the Wisconsin Prognostic Breast Cancer (WPBC) datasets found in literature. The SVM algorithm performs excellently in both problems for the case study datasets, exhibiting high accuracy, sensitivity and specificity indices.

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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Elias Zafiropoulos
  • Ilias Maglogiannis
  • Ioannis Anagnostopoulos
    • 1
  1. 1.Department of Information and Communication Systems EngineeringUniversity of the AegeanKarlovasi, SamosGreece

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