MEDICAL DECISION SUPPORT SYSTEM USING DATA MINING TECHNIQUES MOHEB R. GIRGIS, TAREK M MAHMOUD & ENTESAR H. ELIWA Department of Computer Science, Faculty of Science, Minia University, Minya, Egypt ABSTRACT The healthcare industry collects a huge amount of data which is not properly mined and not put to the optimum use. Discovery of these hidden patterns and relationships often goes unexploited. Advanced data mining modeling techniques can help overcome this situation. The health-care knowledge management especially in heart disease can be improved through the integration of data mining and decision support. This paper presents a prototype heart disease decision support system that uses two data mining classification modeling techniques, namely, Naïve Bayes and Decision Trees. It extracts hidden knowledge from a database containing information about patients with two important heart diseases in Egypt, namely, AMI (Coronary artery), and HTN (High blood pressure) disease. The models are trained and validated against a test dataset. Lift Chart and Classification Matrix methods are used to evaluate the effectiveness of the models. The results showed that the two models are able to extract patterns in response to the predictable state. Five mining goals are defined based on exploration of the two heart diseases dataset and the objectives of this research. The goals are evaluated against the trained models. The two models could answer complex queries, each with its own strength with respect to ease of model interpretation, access to detailed information and accuracy. KEYWORDS: Decision Support Systems, Data Mining, Decision Tree, Naïve Bayes, Medical Health Care INTRODUCTION Decision support systems (DSSs) are defined as interactive application systems which are intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions [1, 2, 3]. They incorporate both data and models and they are designed to assist decision makers in decision making processes. They provide support for decision making, they do not replace it. The mission of decision support systems is to improve effectiveness, rather than the efficiency of decisions. Data mining (DM) is the process of analyzing data in order to discover implicit, but potentially useful information and uncover previously unknown patterns and relationships hidden in data. DM encompasses statistical, pattern recognition, and machine learning tools to support the discovery of patterns, trends and rules that lie within data given [4]. The use of DM to facilitate decision support can lead to the improved performance of decision making and can enable the tackling of new types of problems that have not been addressed before. The integration of DM and decision support can significantly improve current approaches and create new approaches to problem solving, by enabling the fusion of knowledge from experts and knowledge extracted from data [1]. The major challenge facing the healthcare industry is the provision for quality services at affordable costs. A quality service implies diagnosing patients correctly and treating them effectively. Poor clinical decisions can lead to disastrous results which is unacceptable. There is a huge amount of untapped data that can be turned into useful information. Medical diagnosis is known to be subjective; it depends on the physician making the diagnosis. Secondly, and most importantly, the amount of data that should be analyzed to make a good prediction is usually huge and at times International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR) ISSN 2249-6831 Vol. 3, Issue 3, Aug 2013, 169-184 © TJPRC Pvt. Ltd.