The healthcare environment is generally perceived as being ‘information rich' yet ‘knowledge
poor'. Which, unfortunately, are not “mined" to discover hidden information for effective decision
making by healthcare practitioners. The health-care knowledge management can be improved through
the integration of data mining and decision support. In this paper, we present a prototype Hepatitis C
Virus Decision Support System (HCVDSS) that uses three data mining classification techniques,
namely, Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its
own strength in realizing the objectives of the defined mining goals. HCVDSS can answer complex
“what if" queries. Using medical profiles such as gender, residence, Alt and Ast the proposed HCVDSS
can predict the likelihood of patients getting HCV disease. It enables significant knowledge, e.g.,
patterns, relationships between medical factors related to HCV disease, to be established. The proposed
HCVDSS, which is implemented on the .Net platform, is windows application, user-friendly, scalable,
reliable and expandable.