Knowledge discovery for hepatitis C virus diagnosis: A framework for mining interesting classification rules

The objective of this work is to discover the medical knowledge of Hepatitis Virus C in terms of diagnosis issue. Since the treatment of HCV patient is a long term treatment and complicated. Some patients can not be completely cured, whereas some are success. The severity of the disease can be evaluated via the biopsy technique which is limited for those patients who have complication. Therefore, given the blood test collected during the treatment process, it is a challenge problem to find out the knowledge using the biological information obtained from patients' blood. This paper proposes the data abstraction algorithm and the pruning algorithm inorder to extract a set of interesting rules. We found that our rule set is useful for physician in order to diagnos the HCV patient.