Effectiveness of Association Rule Mining Classification in HIV and HCV

Human Immunodeficiency Virus (HIV) and Hepatitis, mainly Hepatitis C Virus (HCV) are most serious difficulties in public health and social life. Many people are chronically infected by HIV and HCV blood-born infection. In India, above 90% of HIV transmissions are linked with either unprotected sexual intercourse or by the distribution of injected equipments between infected and uninfected persons. This paper is to classify the HIV patients from co-infected HIV and HCV patients using rule based classifier, first order inductive learner and predictive rule mining algorithms. The rule based classifier is analyzing the problem on the basis of framing if-else rules. But, First Order Inductive Learner learns one rule from sequential covering method. Predictive rule mining modifies first order inductive learner with consideration of more rules. The performance effectiveness and accuracy of the algorithms in classifying HIV is the part of this study.