Associative Classification of the Jordanian Hospitals Efficiency Based on DEA

Data Envelopment Analysis (DEA) has been widely used in many fields. Recently, it has been adopted by healthcare sector to improve efficiency and performance of the healthcare organizations and thus reducing overall costs and increasing productivity. In this paper, we demonstrate the results of applying the DEA model in Jordanian hospitals. The dataset consists of 28 hospitals and classified into 2 groups: efficient and non-efficient hospitals. We applied different association classification data mining techniques (JCBA, WeightedClassifier and J48) to generate strong rules using WEKA. We also applied the Open Source DEA (OSDEA) software and MaxDEA software to manipulate the DEA model. The results showed that JCBA has the highest accuracy. However, WeightedClassifier method achieves the highest number of generated rules while the JCBA method has the minimum number of generated rules. The findings have several implications for practice in healthcare sector and for decision makers.