Medical decision support system for hepatitis c virus prediction using data mining techniques

Abstract EN

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.