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Applying data-mining techniques for discovering association rules

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Abstract

Data mining has become a hot research topic, and how to mine valuable knowledge from such huge volumes of data remains an open problem. Processing huge volumes of data presents a challenge to existing computation software and hardware. This study proposes a model using association rule mining (ARM) which is a kind of data-mining technique for discovering association rules of chronic diseases from the enormous data that are collected continuously through health examination and medical treatment. This study makes three critical contributions: (1) It suggests a systematical model of exploring huge volumes of data using ARM, (2) it shows that helpful implicit rules are discovered through data-mining techniques, and (3) the results proved that the proposed model can act as an expert system for discovering useful knowledge from huge volumes of data for the references of doctors and patients to the specific chronic diseases prognosis and treatments.

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Authors and Affiliations

  1. Department of Accounting, National Changhua University of Education, Changhua, 50058, Taiwan, R.O.C.

    Mu-Jung Huang

  2. Department of Finance, National Changhua University of Education, Changhua, 50058, Taiwan, R.O.C.

    Hsiu-Shu Sung & Ming-Cheng Wu

  3. Kuang Tien General Hospital, Taichung, 43350, Taiwan, R.O.C.

    Tsu-Jen Hsieh

  4. Department of Business Administration, Meiho University, Neipu Township, 91202, Pingtung, Taiwan, R.O.C.

    Shao-Hsi Chung

Authors
  1. Mu-Jung Huang
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  2. Hsiu-Shu Sung
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  3. Tsu-Jen Hsieh
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  4. Ming-Cheng Wu
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  5. Shao-Hsi Chung
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Correspondence to Mu-Jung Huang.

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This paper does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by Mu-Yen Chen.

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Huang, MJ., Sung, HS., Hsieh, TJ. et al. Applying data-mining techniques for discovering association rules. Soft Comput 24, 8069–8075 (2020). https://doi.org/10.1007/s00500-019-04163-4

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  • DOI: https://doi.org/10.1007/s00500-019-04163-4

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