Abstract :
Rough set theory is a relatively new intelligent technique
used in the discovery of data dependencies; it evaluates
the importance of attributes, discovers the patterns of
data, reduces all redundant objects and attributes, and
seeks the minimum subset of attributes. Moreover, it is
being used for the extraction of rules from databases. In
this paper, we present a rough set approach to attribute
reduction and generation of classification rules from a
set of medical datasets. For this purpose, we first introduce
a rough set reduction technique to find all reducts
of the data that contain the minimal subset of attributes
associated with a class label for classification. To evaluate
the validity of the rules based on the approximation
quality of the attributes, we introduce a statistical test to
evaluate the significance of the rules. Experimental results
from applying the rough set approach to the set of
data samples are given and evaluated. In addition, the
rough set classification accuracy is also compared to
the well-known ID3 classifier algorithm. The study
showed that the theory of rough sets is a useful tool for
inductive learning and a valuable aid for building expert
systems.
