Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

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Duen-Yian Yeh1, Ching-Hsue Cheng This email address is being protected from spambots. You need JavaScript enabled to view it.2 and Chieh-Yi Hsieh2

1Department of Information Management, Transworld University, Yunlin, Taiwan 640, R.O.C.
2Department of Information Management, National Yunlin University of Science & Technology, Yunlin 640, Taiwan, R.O.C.


 

Received: February 13, 2017
Accepted: January 5, 2018
Publication Date: March 1, 2018

Download Citation: ||https://doi.org/10.6180/jase.201803_21(1).0015  

ABSTRACT


Annual medical expenses for end-stage renal disease (ESRD) in Taiwan have exceeded NT$ 34.4 billion. Increasing the treatment quality of hemodialysis (HD) is an extremely crucial medical problem. This study focused on the HD data of patients with ESRD in Taiwan and aimed at mining decision rules and key attributes for effectively monitoring the treatment quality of HD. Five types of rule-based classifiers were adopted to conduct data mining. The main findings included the following: (1) 216 quality decision rules and eight key attributes were mined and an effective diagnosis mechanism was established for contributing to clinical diagnoses; (2) red blood cell being listed as a key attribute and gender being an irrelevant attribute were two of important findings; (3) the classification performance of rough set theory was superior to that of other classifiers. An expert meeting identified the findings that will effectively assist the clinician to monitor the treatment quality of HD.


Keywords: End-stage Renal Disease, Data Mining, Rough Set Theory, Albumin, Hematocrit


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