Vol.6, No.3, August 2017.                                                                                                         ISSN: 2217-8309

                                                                                                                                                 eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Association Rule Mining to Identify Critical Demographic Variables Influencing the Degree of Burnout in A Regional Teaching Hospital


Yii-Ching Lee, Chih-Hsuan Huang, Yi-Chen Lin, Hsin-Hung Wu


© 2017 Yii-Ching Lee et al, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 6, Issue 3, Pages 497-502, ISSN 2217-8309, DOI: 10.18421/TEM63-10, August 2017.




This study uses apriori algorithm of IBM SPSS Modeler 14.1 on nine questions of emotional exhaustion dimension along with ten demographic variables from a regional teaching hospital in Taiwan in 2014 to identify critical demographic variables that influence the degree of burnout. By setting up the support of 25%, confidence of 80%, and lift of 1.5, twenty nine rules are found. To further refine the rules by their similarities, seven major combinations are summarized. The major characteristics are depicted below. Female medical staffs with college/university education who are not in charge of supervisor/manager with very often direct patient contacts feel much stressful to work with people directly and all day. That is, they have relatively higher degree of burnout. In summary, four demographic variables are found to be the major variables that influence emotional exhaustion, including gender, supervisor/manager, education, and direct patient contact.


Keywords – Apriori algorithm, Association rule, Emotional exhaustion, Demographic variable, Burnout.



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