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International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage

SpaCCS 2016: Security, Privacy and Anonymity in Computation, Communication and Storage pp 184–194Cite as

An Analysis of the Survivability in SEER Breast Cancer Data Using Association Rule Mining

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10067))

Abstract

Medical professionals need a reliable methodology to predict the survivability of patients with breast cancer. In this work, a classical association rule mining algorithm-Apriori was adopted for analyzing the related association relationship between medical attributes of records and the survivability of patients. The SEER Dataset was used in this research. After the dataset was preprocessed, 29606 records was obtained. Each record contains 17 breast cancer related attributes. Then apriori algorithm was applied in these preprocessed records, 326 association rules about ‘survived’ and 22 association rules about ‘not survived’ were obtained finally. These discovered association rules indicate that the attributes of EOD-Lymph Node Involv and SEER historic stage A play important roles in the survivability of patients after analyzed and compared.

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Acknowledgments

This study is supported by the China Postdoctoral Science Foundation (2016M592450), and the Hunan Provincial Natural Science Foundation of China (2016JJ4119). Sincerely thanks to the National Cancer Institute, USA for providing the SEER cancer database in public.

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Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Fangfang Li & Yu Duan

Authors
  1. Fangfang Li
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  2. Yu Duan
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Corresponding author

Correspondence to Yu Duan .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Colorado State University, Fort Collins, Colorado, USA

    Indrakshi Ray

  3. University of the West of Scotland, Paisley, Glasgow, United Kingdom

    Jose M. Alcaraz Calero

  4. Indian Institute of Information Technology and Management Kerala (IIITM-K), Trivandrum, Kerala, India

    Sabu M. Thampi

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© 2016 Springer International Publishing AG

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Li, F., Duan, Y. (2016). An Analysis of the Survivability in SEER Breast Cancer Data Using Association Rule Mining. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy and Anonymity in Computation, Communication and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10067. Springer, Cham. https://doi.org/10.1007/978-3-319-49145-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-49145-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49144-8

  • Online ISBN: 978-3-319-49145-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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