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Mining Association Rules of Breast Cancer Based on Fuzzy Rough Set

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Business Intelligence and Information Technology (BIIT 2021)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 107))

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Abstract

In view of the current increasing status of breast cancer patients, to enable patients to predict whether they have breast cancer by themselves from physical examination data, this paper proposes a method for mining breast cancer association rules based on fuzzy rough sets. The method proposed in this paper first analyzes the attributes in the traditional blood data, then applies the attribute reduction of the fuzzy rough set, deletes the attributes irrelevant to breast cancer, and uses the Apriori algorithm in data mining to obtain the frequent items in the remaining attributes Set, apply low support and high confidence to extract many practical, strong association rules. Specific examples verify this method. The experimental results show that this method can dig out more and higher-quality rules compared with traditional algorithms. At the same time, these extracted rules are highly effective reference values in diagnosing and preventing breast cancer.

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Acknowledgment

The author sincerely thanks the research group members for their efforts on this paper and thanks to the editors and reviewers for their valuable comments.

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Authors and Affiliations

  1. Harbin University of Commerce, Harbin, 150028, China

    Min Guo, Tongtong Han, Wenjing Wang & Shiyong Ning

  2. Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, Harbin, 150028, China

    Shiyong Ning

Authors
  1. Min Guo
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  2. Tongtong Han
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  3. Wenjing Wang
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  4. Shiyong Ning
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Corresponding author

Correspondence to Shiyong Ning .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Yaoqun Xu

  3. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhijie Zhao

  4. Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada

    Sabah Mohammed

  5. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, Heilongjiang, China

    Zhipeng Fan

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Guo, M., Han, T., Wang, W., Ning, S. (2022). Mining Association Rules of Breast Cancer Based on Fuzzy Rough Set. In: Hassanien, A.E., Xu, Y., Zhao, Z., Mohammed, S., Fan, Z. (eds) Business Intelligence and Information Technology. BIIT 2021. Lecture Notes on Data Engineering and Communications Technologies, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-030-92632-8_21

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