Open AccessAssociative classification of the Jordanian hospitals efficiency based on DEA
Author(s) -
Ahmad Alaiad,
Hassan Najadat,
Nusaiba Al-Mnayyis,
Ashwaq Khalil
Publication year - 2018
Publication title -
global journal of computer sciences
Language(s) - English
Resource type - Journals
ISSN - 2301-2587
DOI - 10.18844/gjcs.v8i3.4022
Subject(s) - data envelopment analysis , computer science , productivity , health care , software , c4.5 algorithm , association rule learning , data mining , operations research , artificial intelligence , statistics , mathematics , economics , support vector machine , naive bayes classifier , macroeconomics , programming language , economic growth
Data envelopment analysis (DEA) has been widely used in many fields. Recently, it has been adopted by the healthcare sector to improve efficiency and performance of the healthcare organisations, 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 is classified into two groups: efficient and non-efficient hospitals. We applied different association classification data mining techniques (JCBA, WeightedClassifier and J48) to generate strong rules using the Waikato Environment for Knowledge Analysis. We also applied the open source DEA 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 results have several implications for practice in the healthcare sector and decision makers. Keywords: Component, DEA, DMU, output-oriented model, health care system.