Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining

Md Faisal Kabir, Simone A. Ludwig, Abu Saleh Abdullah

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    Breast cancer is the most common cancer in women worldwide. Prevention of breast cancer through risk factors reduction is a significant concern to decrease its impact on the population. Attaining or detecting significant information in the form of rules is the key to prevent breast cancer. Our objective is to find hidden but important knowledge of the form of rules from the risk factors data set of breast cancer. Mining rules is one of the vital tasks of data mining as rules provide concise statement of potentially important information that is easily understood by end users. In this paper, we use association rule mining, a data mining technique to attain information in the form of rules from breast cancer risk factors data that could be useful to initiate prevention strategies. We discovered rules of both breast cancer and non-breast cancer patients so that we can understand and compare the characteristics of both breast cancer and non-breast cancer individuals. The experimental results show that generated or mined rules hold the highest confidence level.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
    EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2433-2441
    Number of pages9
    ISBN (Electronic)9781538650356
    DOIs
    StatePublished - Jan 22 2019
    Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
    Duration: Dec 10 2018Dec 13 2018

    Publication series

    NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

    Conference

    Conference2018 IEEE International Conference on Big Data, Big Data 2018
    CountryUnited States
    CitySeattle
    Period12/10/1812/13/18

    All Science Journal Classification (ASJC) codes

    • Computer Science Applications
    • Information Systems

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