A Framework for Intelligent Medical Diagnosis using Rough Set with Formal Concept Analysis
This work addresses the challenge of rule minimization in medical diagnosis systems, which is incremental as it builds on existing rough set methods by integrating formal concept analysis.
The paper tackles the problem of generating too many decision rules in medical diagnosis using rough set theory by introducing a two-step framework that combines rough set theory for rule mining and formal concept analysis for exploring attribute relationships, resulting in improved knowledge extraction and identification of key decision factors.
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 them selves. 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 such models are failed to include important aspects of the expert decisions. Therefore, an effort has been made to process inconsistencies in data being considered by Pawlak with the introduction of rough set theory. Though rough set has major advantages over the other methods, but it generates too many rules that create many difficulties while taking decisions. Therefore, it is essential to minimize the decision rules. In this paper, we use two processes such as pre process and post process to mine suitable rules and to explore the relationship among the attributes. In pre process 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.