(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 2, No.3, March 2011 34 | Page http://ijacsa.thesai.org/ Rule Base Fuzzifier Defuzzifier Inference A Fuzzy Decision Support System for Management of Breast Cancer Ahmed Abou Elfetouh Saleh Dept. of Information Systems, Faculty of computer and information system Mansoura University Mansoura, Egypt elfetouh@gmail.com Sherif Ebrahim Barakat Dept. of Information Systems, Faculty of computer and information system Mansoura University Mansoura, Egypt sherifiib@yahoo.com Ahmed Awad Ebrahim Awad Dept. of Information Systems, Faculty of computer and information system Mansoura University Mansoura, Egypt ahmedaweb@yahoo.com Abstract— In the molecular era the management of cancer is no more a plan based on simple guidelines. Clinical findings, tumor characteristics, and molecular markers are integrated to identify different risk categories, based on which treatment is planned for each individual case. This paper aims at developing a fuzzy decision support system (DSS) to guide the doctors for the risk stratification of breast cancer, which is expected to have a great impact on treatment decision and to minimize individual variations in selecting the optimal treatment for a particular case. The developed system was based on clinical practice of Oncology Center Mansoura University (OCMU) This system has six input variables (Her2, hormone receptors, age, tumor grade, tumor size, and lymph node) and one output variable (risk status). The output variable is a value from 1 to 4; representing low risk status, intermediate risk status and high risk status. This system uses Mamdani inference method and simulation applied in MATLAB R2009b fuzzy logic toolbox. Keywords: Decision Support System; Breast Cancer; Fuzzy Logic; Mamdani Inference; I. INTRODUCTION In recent years, the methods of Artificial Intelligence have largely been used in the different areas including the medical applications. In the medical field, many decision support systems (DSSs) were designed, as Aaphelp, Internist I, Mycin, Emycin, Casnet/Glaucoma, Pip, Dxplain, Quick Medical Reference, Isabel, Refiner Series System and PMA [1,2,3,4,5,6,7] which assist physicians in their decisions for diagnosis and treatment of different diseases. In cancer management many DSSs have been developed as ONCOCIN [1], OASIS, Lisa [8, 9]. The diagnosis of disease involves several levels of uncertainty and imprecision [10]. According to Aristotelian logic, for a given proposition or state we only have two logical values: true-false, black-white, 1-0. In real life, things are not either black or white, but most of the times are grey. Thus, in many practical situations, it is convenient to consider intermediate logical values. Uncertainty is now considered essential to science and fuzzy logic is a way to model and deal with it using natural language. We can say that fuzzy logic is a qualitative computational approach. Fuzzy logic is a method to render precise what is imprecise in the world of medicine. Many medical applications use fuzzy logic as CADIAG [11], MILORD [11], DOCTORMOON [12], TxDENT [13], MedFrame/CADIAG-IV [14], FuzzyTempToxopert [14] and MDSS [15]. In the field of breast cancer, DSS is very important, as breast cancer is the most common cause of cancer death among women worldwide, in Egypt, breast cancer is the most common cancer among women; representing 18.9% of total cancer cases [16]. The National Cancer Institute (NCI) reported a series of 10556 patients with breast cancer during the year 2001. The diagnoses have a lot of confounding alternatives, some of them are uncertain as Her2-neu positivity, hormone receptor status and age. Therefore the treatment planning is based on the interaction of a lot of compound variables with complex outcomes. We planned to use fuzzy logic to deal with uncertainty for diagnosis risk status of breast cancer. This paper is organized as follows; general structure of fuzzy logic system is introduced in section II, design of the system is presented in section III and test system and discussion are presented in section IV. II. GENERAL STRUCTURE OF FUZZY LOGIC SYSTEM Fuzzy logic system as seen in Fig. 1 consists of the following modules [17]: Figure 1. Structure of Fuzzy Logic System. 1. Fuzzification: - is the operation of transforming a crisp set to a fuzzy set. The operation translates crisp