Paper Info
Title
A FRAMEWORK FOR INTELLIGENT MEDICAL DIAGNOSIS USING ROUGH SET WITH FORMAL CONCEPT ANALYSIS
Abstract
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.
Year
DOI
Venue
2013
10.5121/ijaia.2011.2204
International Journal of Artificial Intelligence & Applications
DocType
Volume
Issue
Journal
2
2
ISSN
Citations 
PageRank 
International Journal of Artificial Intelligence & Applications (IJAIA), Vol.2, No.2, April 2011
8
0.53
References 
Authors
5
3
Name
Order
Citations
PageRank
B. K. Tripathy19018.13
D. P. Acharjya2608.98
V. Cynthya380.53