Detecting road maps for capacity utilization decisions by Clustering Analysis and CHAID Decision Trees.
The aims of this study are to provide a standard CUR value, to determine financial and organizational factors which affect the capacity utilization and develop road maps for increasing capacity utilization. To reach these aims by an objective method, we used data mining method that discovers hidden and useful pattern in a large amount of data. Two different method of data mining were used in two stages for this study. In first step, standard value of CUR was determined by K-means Clustering Analysis. CHAID Decision Tree Algorithm as a second method was implemented for determination of impact factors that provided steps for road maps. The study was concerned Turkish Ministry of Health public hospitals. 592 hospitals were covered and financial and operational data of the year 2004 were used in the study. Finally two different road maps were developed and suggestions were made according the results of the study.
Koyuncugil
Ali Serhan
AS
Capital Markets Board of Turkey, Department of Research, Eskisehir, Ankara, Turkey. askoyuncugil@gmail.com
Ozgulbas
Nermin
N
D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D003663:Decision Trees; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data*; D006779:Hospitals, Public / Q000706:statistics & numerical data* / Q000639:trends; D006801:Humans; D012040:Regional Health Planning / Q000379:methods*; D055872:Surge Capacity / Q000706:statistics & numerical data* / Q000639:trends; D014421:Turkey