Noninvasive prediction of HCV 4 SVR by 2D US: a randomizedstudy using data mining algorithm

Objective and aim :Hepatitis C virus [HCV] can cause both acute and chronic hepatitis. Antiviral therapy is thecornerstone for the treatment of chronic HCV infection once diagnosis is confirmed by PCR. Thegoal of antiviral therapy is to eradicate HCV RNA or attain sustained virological response [SVR]. In many countries worldwide, including Egypt, HCV infection is treated with a combination of pegylated interferon [and ribavirin [RBV]. Liver fibrosis/cirrhosis stage influences theresponse to pegylated interferon [and RBV. Even with new oral the rapies such as Sovaldimany patients have to continue to be on combination regimens of interferon/RBV or RBV alone. In the current study, we aimed to use data mining analysis to determine sonographic picturesthat can successfully predict SVR in HCV-4 patients before the antiviral therapy

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Methods: Eighty-two patients were enrolled in this study and they underwent two-dimensional ultrasound examination before the antiviral therapy. The sonographic data obtained were analyzed with Rapidminer version 4.6 to create a decision tree algorithm for the prediction of SVR

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Results: The absence of significant liver fibrosis was a predictive parameter of SVR mainly in those patients without a sonographic picture of cirrhosis. The resulting tree yielded an accuracy, sensitivity, and specificity of 85.82 +/- 10.79, 68.75, and 96.00%, respectively, upon 10-foldcross-validation

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Conclusion: In the current study we used decision tree algorithm, one of the most important computational methods and tools for data analysis and predictive modeling in applied medicine, to predict SVR in HCV-infected patients. Two-dimensional ultrasound can give predictive information regarding the treatment outcome before interferon therapy for HCV-4