A FRAMEWORK FOR INTELLIGENT MEDICAL DIAGNOSIS USING ROUGH SET WITH FORMAL CONCEPT ANALYSIS

Medical diagnosis process vary in the degree to which they attempt to deal with different complicating aspects of diagnosis such as relative importance of symptoms, varied symptom pattern and the relation between diseases themselves. Based on decision theory, in the past many mathematical models such as crisp set, probability distribution, fuzzy set, intuitionistic fuzzy set were developed to deal with complicating aspects of diagnosis. But, many suchmodels are failed to include important aspects of the expert decisions. Therefore, an effort has been made to processinconsistencies in data being considered by Pawlak with the introduction of rough set theory. Though rough set hasmajor advantages over the other methods, but it generates too many rules that create many difficulties while takingdecisions. Therefore, it is essential to minimize the decision rules. In this paper, we use two processes such as preprocess and post process to mine suitable rules and to explore the relationship among the attributes. In pre processwe use rough set theory to mine suitable rules, whereas in post process we use formal concept analysis from thesesuitable rules to explore better knowledge and most important factors affecting the decision making