Table of Contents
Advances in Artificial Neural Systems
Volume 2012 (2012), Article ID 835903, 11 pages
http://dx.doi.org/10.1155/2012/835903
Research Article

Hemodialysis Key Features Mining and Patients Clustering Technologies

Department of Information Management, Chaoyang University of Technology, Wufeng District, Taichung 41349, Taiwan

Received 3 March 2012; Revised 4 June 2012; Accepted 8 June 2012

Academic Editor: Anke Meyer-Baese

Copyright © 2012 Tzu-Chuen Lu and Chun-Ya Tseng. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The kidneys are very vital organs. Failing kidneys lose their ability to filter out waste products, resulting in kidney disease. To extend or save the lives of patients with impaired kidney function, kidney replacement is typically utilized, such as hemodialysis. This work uses an entropy function to identify key features related to hemodialysis. By identifying these key features, one can determine whether a patient requires hemodialysis. This work uses these key features as dimensions in cluster analysis. The key features can effectively determine whether a patient requires hemodialysis. The proposed data mining scheme finds association rules of each cluster. Hidden rules for causing any kidney disease can therefore be identified. The contributions and key points of this paper are as follows. (1) This paper finds some key features that can be used to predict the patient who may has high probability to perform hemodialysis. (2) The proposed scheme applies k-means clustering algorithm with the key features to category the patients. (3) A data mining technique is used to find the association rules from each cluster. (4) The mined rules can be used to determine whether a patient requires hemodialysis.