Hybrid Rough Genetic algorithm model for making treatment decisions of hepatitis C

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 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 hybrid Rough-Genetic algorithm model that uses the advantages of Rough set as a powerful analysis tool to identify the most relevant attributes. Rough sets are used to generate a set of reducts which consist of a minimal set of attributes and induce a set of decision rules. On the other hand, Genetic Algorithm is used to optimize and improve the rules induced using Rough Sets for classifying studied cases for testing new medication for HCV treatment. The experimental results obtained, show that the overall classification accuracy offered by the proposed Model is a superlative result.