DESIGN OF A FUZZY MODEL FOR THALASSEMIA DISEASE DIAGNOSIS: USING MAMDANI TYPE FUZZY INFERENCE SYSTEM (FIS) Original Article SAPNA THAKUR*, SHARADA NANDAN RAW, RAVINDRA SHARMA Department of Mathematics, National Institute of Technology, Raipur, Chhattisgarh, India Email: sapnarajput85@gmail.com Received: 01 Feb 2015 Revised and Accepted: 01 Mar 2016 ABSTRACT Objective: Diagnosis process of Thalassemia requires several types of medical test, and results of this test together identify the stage of Thalassemia. The objective of this study is to design a Fuzzy Inference System to diagnose the severity of the Thalassemia disease of a patient by using Fuzzy Logic. Methods: In this paper, a new approach based on fuzzy inference system was presented for prediction of Thalassemia disease in patients. The proposed Fuzzy model combined the expert’s knowledge and the fuzzy logic approach which is then combined in fuzzy rule base to diagnose the presence of the disease. The performances of the system graphically represented by fuzzy inference system tools in MATLAB8.4. Results: It was found that our program matched the doctor’s diagnosis in 12 cases perfectly. The other 3 were marginally off. This results with an accuracy of about 80 %. Conclusion: The result suggests that the model provides the most effective way to identify Thalassemia type in patients. The results in this work can be obtained by a simple and inexpensive method. This would generate, in economic terms, significant savings. Keywords: CBC Test, Fuzzy Logic, Mamdani Fuzzy Inference System, Thalassemia Disease © 2016 The Authors. Published by Innovare Academic Sciences Pvt Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ ) INTRODUCTION Anemia is a condition where the number of healthy RBC in the blood is lower than normal. It is due to low RBC’s, destruction of RBC’s or loss of too many RBC’s. If your blood does not have enough RBC’s, your body doesn’t get enough oxygen it needs. As a result, you may feel tired and other symptoms. But sometimes it is very difficult to detect Thalassemia on the basis of symptoms only. In the domain of Thalassemia, there is no such boundary between what is healthy and what is diseased. Having so many factors to detect Thalassemia makes doctor’s work difficult. So, experts require an accurate tool that considering these risk factors and give some certain result in uncertain terms. Some biochemical tests (HGB, HbF, HbA2, RBC, MCV, and MCH) are useful for identifying carriers of the Thalassemia trait [1-3]. In the presence of Thalassemia parameters in the CBC, an accurate and precise quantification of hemoglobin HbA2 is essential for the diagnosis of the Thalassemia trait. When biochemical tests are not exhaustive, it is necessary to study the molecular globin genes [4]. HPLC and electrophoresis are a gold standard for the diagnosis of β-Thalassemia trait, but it is not available at all places. Thus, several attempts have been made to diagnose the condition by using red cell indices. In the present study Hemoglobin (HGB), Mean Corpuscular Volume (MCV) and Mean corpuscular hemoglobin (MCH) values were able to detect cases of type of Thalassemia. In this study, we focus on a development of the first knowledge representation corresponding to the CBC test results to create a mathematical model for Thalassemia disease diagnosis using FIS. The developed FIS model for Thalassemia disease will be useful and beneficial for many informatics related Thalassemia tasks in the future. The objective of this study is to create a fuzzy inference system to predict the severity involve in Thalassemia disease. Three steps are used to monitor general health and Thalassemia. But we are focusing only on the Tests and Procedures. Three steps are as follows: • Medical and Family Histories • Physical Exam • Tests and Procedures. The rest of the paper is organized as follows. An application of Fuzzy expert system in different areas represented in the Introduction section. Materials and Methods section provides the description of fuzzy model and structure of a medical expert system on Thalassemia disease. Finally, the result and discussion are concluded in the Results and Discussion section. Applications of fuzzy logic in different areas In 1965, Prof. Lotfi Zadeh developed fuzzy set theory that emerges the concept of fuzzy logics [5, 6]. Fuzzy logic consists of probabilistic logic or many-valued logics. Rather than fixed and exact reasoning, it provides approximate reasoning. In the development of medical systems, since the 1980s, fuzzy logic is being extensively used. In the last few decades, the significant development of control system theory can be witnessed by which development of computers and electronic has outcome into many different applications of control system theory [7]. When the studies in the literature related to this classification application are examined, it can be seen that a great variety of methods were used. Among these, [8] Fuzzy System have been used to diagnose the different types of anemia on the basis of symptoms such as Irritability, tachycardia, Memory weakness, Bleeding and Chronic fatigue. Another, [9] diagnose Liver disease using fuzzy logic on the basis of CBC Test, which uses 4 parameters such as WBC, HGB, HCT and PLT. [10] Adeli and Neshat proposed a system to diagnose the heart disease using fuzzy logic. [11] Lavanya et. al. also develop a fuzzy expert system to diagnose the Lung Cancer. In this paper, a Fuzzy Inference System is designed to diagnose the severity of the Thalassemia disease of a patient by using Fuzzy Logic. MATERIALS AND METHODS We describe the designing of the Fuzzy Inference System (FIS) for Thalassemia Disease Diagnosis. Design a fuzzy logic system for thalassemia disease diagnosis Problem Specification and Define linguistic Variables: There are 3 input variables and 1 output variable. International Journal of Pharmacy and Pharmaceutical Sciences ISSN- 0975-1491 Vol 8, Issue 4, 2016