IJICIS, Vol.15, No. 2 APRIL 2015 45 International Journal of Intelligent Computing and Information Sciences USING ROUGH SET AND BOOSTING ENSEMBLE TECHNIQUES TO ENHANCE CLASSIFICATION PERFORMANCE OF HEPATITIS C VIRUS M. E. Helal M. Elmogy R. M. Al-Awady Information Systems Deprtmant., Faculty of Computers and Information, Mansoura University, Egypt Information Technology Deprtmant., Faculty of Computers and Information, Mansoura University, Egypt Electronics and Communications Deprtmant., Faculty of Engineering, Mansoura University, Egypt mehelal84@gmail.com melmogy@mans.edu.eg actt_egypt@yahoo.com Abstract-Machine learning techniques have been extensively applied to help medical experts in making a diagnosis of many diseases. Classification is a machine learning technique that is used to forecast the relationship between data samples and classes. It is an essential task in different applications, such as image classification and medical diagnosis. There are different classification techniques, such as SVM, C5.0, Neural Network, K-Nearest Neighbor, and Naive Bayes Classifier. Feature selection for classification of cancer data means discovering feature values of malignant tumors and benign ones. It also means using this knowledge to forecast the state of new cases. In this paper, we use Rough sets as a feature selection technique to create a subset feature from the original features. Therefore, we use the resulting subset with different classification and ensemble techniques to discover classes of unknown data using HCV data set. SVM, C5.0, and Ensemble classifiers are used as classification techniques to discover classes of unknown data. In this paper, the percentage of accuracy, sensitivity, and specificity are used as evaluation parameters for the tested classification techniques. Experimental results show that the proposed hybrid RS-Boosting/SVM technique has higher accuracy, sensitivity and specificity rates with selected subset features than other tested techniques. Keywords: Rough Set theory (RST), Feature Selection, Classification, Ensemble Classifier, C5.0, Support Vector Machine (SVM), Hepatitis C Virus (HCV). 1. Introduction Machine learning technologies have become well suited for analyzing data. In the medical area, classification and treatment are the main tasks for a physician. Machine learning studies are concentrating on learning how to recognize complicated patterns and create intelligent decisions depending on the tested data [1]. Medical data consists of attributes where missing value and redundant information need to be discarded. In medical domain, one of the most fundamental requirements for feature selection and classification is the ability to deal with inconsistent and imprecise information due to the considerable quantity of noisy, unrelated or misleading features [2]. Medical data analysis is a complicated task because it requires knowledge from the medical data set as well as advanced techniques for processing,