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 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.
