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RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system

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This article was retracted on 22 January 2021

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Abstract

Breast cancer is the top cancer in women worldwide. Scientists are looking for early detection strategies which remain the cornerstone of breast cancer control. Consequently, there is a need to develop an expert system that helps medical professionals to accurately diagnose this disease. Artificial immune recognition system (AIRS) has been used successfully for diagnosing various diseases. However, little effort has been undertaken to improve its classification accuracy. To increase the classification accuracy, this study introduces a new hybrid system that incorporates support vector machine, fuzzy logic, and real tournament selection mechanism into AIRS. The Wisconsin Breast Cancer data set was used as the benchmark data set; it is available through the machine learning repository of the University of California at Irvine. The classification performance was measured through tenfold cross-validation, student’s t test, sensitivity and specificity. With an accuracy of 100 %, the proposed method was able to classify breast cancer dataset successfully.

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Acknowledgments

This work is supported by University of Malaya Research Grant no vote RP0061-13ICT.

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Authors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kula Lumpur, Malaysia

    Mahmoud Reza Saybani, Teh Ying Wah & Saeed Reza Aghabozorgi

  2. Department of Computer Networks, Markaz-e Elmi Karbordi Bandar Abbas 1, University of Applied Science, Bandar Abbas, Iran

    Mahmoud Reza Saybani

  3. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Shahaboddin Shamshirband & Miss Laiha Mat Kiah

  4. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, 310130, Arad, Romania

    Valentina Emilia Balas

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  1. Mahmoud Reza Saybani
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  2. Teh Ying Wah
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  3. Saeed Reza Aghabozorgi
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  4. Shahaboddin Shamshirband
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Correspondence to Mahmoud Reza Saybani or Shahaboddin Shamshirband.

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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Saybani, M.R., Wah, T.Y., Aghabozorgi, S.R. et al. RETRACTED ARTICLE: Diagnosing breast cancer with an improved artificial immune recognition system. Soft Comput 20, 4069–4084 (2016). https://doi.org/10.1007/s00500-015-1742-1

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