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Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS-TSK

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Explainable AI and Other Applications of Fuzzy Techniques (NAFIPS 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 258))

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Abstract

Developing an accurate system to analyze breast cancer image data can give doctors an extra measure of confidence in assessing patients and can be used to scan all past scans in a clinic database to understand if any patients are at risk. Fuzzy Logic Systems are well suited to building knowledge bases and rule bases that can accurately approximate expert human knowledge, such as a doctor who routinely diagnoses breast cancer in patients. Genetic Algorithms boost the capability of a Fuzzy Logic System, especially when given a representative data set of the system, by using a subset of the data to learn the optimal membership functions and rule base of the Fuzzy Logic System. The combination of these two techniques can be utilized to develop a highly accurate approximator of cancer diagnosis.

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References

  1. Gharagyozyan, H.: A practical application of machine learning in medicine. https://www.macadamian.com/learn/a-practical-application-of-machine-learning-in-medicine/

  2. Murphy, A.: Breast cancer wisconsin (diagnostic) data analysis using GFS, version 1.0.0, February 2021. https://www.mathworks.com/matlabcentral/fileexchange/90556-breast-cancer-wisconsin-diagnostic-data-analysis-using-gfs

  3. Agarap, A.F.M.: On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset. https://www.arxiv-vanity.com/papers/1711.07831/

  4. U.S. Department of Health & Human Services. Centers for disease control & prevention. https://www.cdc.gov/cancer/breast/index.htm

  5. Wolberg, W.H.: 2. UCI machine learning repository wisconsin breast cancer (diagnostic) data set. http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29

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Author information

Authors and Affiliations

  1. Department of Aerospace Engineering, University of Cincinnati, Cincinnati, OH, USA

    Allison Murphy

Authors
  1. Allison Murphy
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Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Julia Rayz

  2. Purdue University, West Lafayette, IN, USA

    Victor Raskin

  3. University of Alberta, Edmonton, AB, Canada

    Scott Dick

  4. University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Murphy, A. (2022). Breast Cancer Wisconsin (Diagnostic) Data Analysis Using GFS-TSK. In: Rayz, J., Raskin, V., Dick, S., Kreinovich, V. (eds) Explainable AI and Other Applications of Fuzzy Techniques. NAFIPS 2021. Lecture Notes in Networks and Systems, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-82099-2_27

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