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Supporting Breast Cancer Diagnosis with Multi-objective Genetic Algorithm for Outlier Detection

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Advanced Solutions in Diagnostics and Fault Tolerant Control (DPS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 635))

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Abstract

Outlier detection in medical data covers a broad spectrum of medical research. In this paper, the authors propose a new approach to outlier detection based on the multi-objective genetic algorithm. In medical data, an outlier may be considered as a deviation which indicates the existence of cancerous cells in the breast. The paper presents the results of tests which were conducted on the set of medical data from the repository. The results of the study indicate that our method can be successfully applied to the medical problem in question.

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Acknowledgment

The database used in experiments was taken from the UCI Machine Learning Repository [21].

Author information

Authors and Affiliations

  1. Institute of Information Technology, Lodz University of Technology, Lodz, Poland

    Agnieszka Duraj & Lukasz Chomatek

Authors
  1. Agnieszka Duraj
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  2. Lukasz Chomatek
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Corresponding author

Correspondence to Lukasz Chomatek .

Editor information

Editors and Affiliations

  1. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Jan M. Kościelny

  2. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Michał Syfert

  3. Faculty of Mechatronics, Institute of Automatic Control and Robotics, Warsaw University of Technology, Warsaw, Poland

    Anna Sztyber

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Duraj, A., Chomatek, L. (2018). Supporting Breast Cancer Diagnosis with Multi-objective Genetic Algorithm for Outlier Detection. In: Kościelny, J., Syfert, M., Sztyber, A. (eds) Advanced Solutions in Diagnostics and Fault Tolerant Control. DPS 2017. Advances in Intelligent Systems and Computing, vol 635. Springer, Cham. https://doi.org/10.1007/978-3-319-64474-5_25

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  • DOI: https://doi.org/10.1007/978-3-319-64474-5_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64473-8

  • Online ISBN: 978-3-319-64474-5

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