Visualization of rough set decision rules for medical diagnosis systems

Abstract

An ability of Pawlak's Rough Sets Theory to handle imprecision and uncertainty without any need of preliminary or additional information about analyzed data makes this theory very interesting for analyzing medical data. Using Rough Sets Theory knowledge extracted from raw data may be stored in form of decision rules. But increasing number and complexity of decision rules make their analysis and validation by domain experts difficult. In this paper we focus on this problem and propose an approach to visualize decision rules in form of decision trees. Afterwards domain experts validate transformed decision trees and compare the results with general guidelines proposed by the American College of Cardiology Foundation and the American Heart Association. © Springer-Verlag Berlin Heidelberg 2007.