ROUGHSET IMPLEMENTATION FOR RHEUMATOID ARTHRITIS DATASET

Abstract

In the medical field⁴ for the diagnosis process of rheumatoid arthritis disease is a tedious job to deal with different complicating attributes such as the relative importance of symptoms, varied pattern of symptoms and the relation between the rheumatoid arthritis diseases. Based on decision theory, many mathematical models such as crisp set, probability distribution, intuitionistic fuzzy set, fuzzy set, were used to deal with complicating aspects of diagnosis on large dataset. But, they are failed to include important aspects of the expert decisions. Therefore, an effort has been made to process inconsistencies, data being considered by Pawlak with the introduction of rough set theory. Rough set theory has major advantages over the other methods, but it generates too many rules that create difficulties while taking decisions for correct results. Therefore, we have to minimize the steps in decision rules. In this paper, we use two processes such as preprocess and post process to make suitable rules and to explore the relationship among the attributes. In preprocess, we use rough set theory to mine suitable rules, whereas in post process we use formal concept analysis from these suitable rules to explore better knowledge and most important factors affecting the decision making.