Applying data-mining techniques for discovering association rules

  • Mu-Jung HuangEmail author
  • Hsiu-Shu Sung
  • Tsu-Jen Hsieh
  • Ming-Cheng Wu
  • Shao-Hsi Chung


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.


Data mining Association rule mining (ARM) Chronic diseases 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

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


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of AccountingNational Changhua University of EducationChanghuaTaiwan, R.O.C.
  2. 2.Department of FinanceNational Changhua University of EducationChanghuaTaiwan, R.O.C.
  3. 3.Kuang Tien General HospitalTaichungTaiwan, R.O.C.
  4. 4.Department of Business AdministrationMeiho UniversityNeipu TownshipTaiwan, R.O.C.

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