Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.

Citation:
Ahmed, Z., M. A. Salama, H. Hefny, and A. E. Hassanien, "Rough Sets-Based Rules Generation Approach: A Hepatitis C Virus Data Sets.", Advanced Machine Learning Technologies and Applications (AMLTA), Cairo Egypt, 8-10 Dec. , 2012.

Date Presented:

8-10 Dec.

Abstract:

The risk of hepatitis-C virus is considered as a challenge in
the field of medicine. Applying feature reduction technique and generating
rules based on the selected features were considered as an important
step in data mining. It is needed by medical experts to analyze the generated
rules to find out if these rules are important in real life cases.
This paper presents an application of a rough set analysis to discover
the dependency between the attributes, and to generate a set of reducts
consisting of a minimal number of attributes. The experimental results
obtained, show that the overall accuracy offered by the rough sets is high.

PreviewAttachmentSize
3220052.pdf173.07 KB