Rough Based Granular Computing Approach for Making Treatment Decisions of Hepatitis C

Abstract

Hepatitis C virus is a massive health issue affecting significant portions of the world’s population. Applying data pre-processing, feature reduction techniques and generating rules based on the selected features for classification tasks are considered as important steps in the knowledge discovery area in databases. Medical experts analyze the generated rules to find out the most significant rules to apply in order to classify unseen real life cases. This paper highlights a rough set as a powerful analysis tool based on granular computing framework to identify the most relevant attributes, generate a set of reducts which consist of a minimal set of attributes and induce a set of rules for classifying studied cases for testing new drugs for HCV treatment . The experimental results obtained, show that the overall classification accuracy offered by the proposed approach is highly based on generated rules during Hepatitis C treatment.