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Year : 2016  |  Volume : 14  |  Issue : 1  |  Page : 14-18

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

1 Department of Tropical Medicine, Gastroenterology and Hepatology, Al Azhar University, Aswan, Egypt
2 Department of Immunology and Rheumatology, Aswan Hospital of Febrile, Diseases, Aswan, Egypt
3 Department of Gynecology and Obstetrics, Al Azhar University, Aswan, Egypt
4 Department of Internal Medicine, Al Azhar University, Aswan, Egypt
5 Department of General and Bariatric Surgery, Faculty of Medicine, Al Azhar University, Aswan, Egypt
6 Department of Biochemistry, Faculty of Pharmacy, Suez Canal University, Cairo, Egypt

Correspondence Address:
Hamdy Mahfouz
Tropical Medicine, Department of Tropical Medicine, Gastroenterology and Hepatology, Faculty of Medicine, Al Azhar University, Cairo
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/1687-1693.180454

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Objective and aim Hepatitis C virus (HCV) can cause both acute and chronic hepatitis. Antiviral therapy is the cornerstone for the treatment of chronic HCV infection once diagnosis is confirmed by PCR. The goal 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 α and ribavirin (RBV). Liver fibrosis/cirrhosis stage influences the response to pegylated interferon α and RBV. Even with new oral therapies such as Sovaldi many 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 pictures that can successfully predict SVR in HCV-4 patients before the antiviral therapy. 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. 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-fold cross-validation. 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.

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