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An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers

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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 techniques. In this paper we propose a Support Vector Machines (SVMs) based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease. The paper provides the implementation details along with the corresponding results for all the assessed classifiers. Several comparative studies have been carried out concerning both the prognosis and diagnosis problem demonstrating the superiority of the proposed SVM algorithm in terms of sensitivity, specificity and accuracy.

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Authors and Affiliations

  1. Department of Information and Communication Systems Engineering, University of the Aegean, 83200, Karlovasi, Samos, Greece

    Ilias Maglogiannis, Elias Zafiropoulos & Ioannis Anagnostopoulos

Authors
  1. Ilias Maglogiannis
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  2. Elias Zafiropoulos
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  3. Ioannis Anagnostopoulos
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Correspondence to Ilias Maglogiannis.

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Maglogiannis, I., Zafiropoulos, E. & Anagnostopoulos, I. An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl Intell 30, 24–36 (2009). https://doi.org/10.1007/s10489-007-0073-z

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  • DOI: https://doi.org/10.1007/s10489-007-0073-z

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