Abstract
This paper describes processing of the medical data by means of the prediction system based on Rough Set Theory (RST). The Rough Sets proved to be very useful for the analysis of the decision problems concerning objects described in a data table by a set of condition attributes as well as a set of decision attributes. In order to make efficient data analysis and suggestive predictions in a case of the data of patients suffering from viral hepatitis were used to predict a probability of their death or serious disability. This paper also demonstrates an extension of the Rough Set methodology for reducing number of input data in order to increase prediction accuracy without loss of knowledge.
Highlights
IntroductionLower approximation _B_(X__) is the complete set of objects in U which can be certainly classified as the elements in X by using the set of attributes B
The Rough Sets and their theory have been developed as a way of dealing with incomplete sets of information in the early eighties by Zdzislaw Pawlak
The Rough Set Theory has led to many interesting applications and extensions
Summary
Lower approximation _B_(X__) is the complete set of objects in U which can be certainly classified as the elements in X by using the set of attributes B. It is the largest subset of B contained in X. If a set has _B(X) ϭ B(X) ϭ X, the set is precisely called the crisp and for its every element the relationship: xʦXʦU is valid. It is represented by the formula: Card B_Xi nB _Xi =. When 0 Յ μB Յ 1, and if μB ϭ 1 X is a crisp in respect to B
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