TY - JFULL AU - Enas M. F. El Houby and Marwa S. Hassan PY - 2013/12/ TI - Comparison between Associative Classification and Decision Tree for HCV Treatment Response Prediction T2 - International Journal of Biomedical and Biological Engineering SP - 713 EP - 718 VL - 7 SN - 1307-6892 UR - https://publications.waset.org/pdf/17322 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 83, 2013 N2 - Combined therapy using Interferon and Ribavirin is the standard treatment in patients with chronic hepatitis C. However, the number of responders to this treatment is low, whereas its cost and side effects are high. Therefore, there is a clear need to predict patient’s response to the treatment based on clinical information to protect the patients from the bad drawbacks, Intolerable side effects and waste of money. Different machine learning techniques have been developed to fulfill this purpose. From these techniques are Associative Classification (AC) and Decision Tree (DT). The aim of this research is to compare the performance of these two techniques in the prediction of virological response to the standard treatment of HCV from clinical information. 200 patients treated with Interferon and Ribavirin; were analyzed using AC and DT. 150 cases had been used to train the classifiers and 50 cases had been used to test the classifiers. The experiment results showed that the two techniques had given acceptable results however the best accuracy for the AC reached 92% whereas for DT reached 80%. ER -