Using data mining techniques to determine predictors of hepatitis C virus treatment response / AbuBakr Hussien Mahmoud Awad ; Supervised Ibrahim Farag Abdelrahman , Ahmed Hussien Kamal
Material type:
- إستخدام تقنيات التنقيب فى البيانات لتحديد مستدلات الإستجابة لعلاج الفيروس الكبدى سى [Added title page title]
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قاعة الرسائل الجامعية - الدور الاول | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.03.M.Sc.2012.Ab.U (Browse shelf(Opens below)) | Not for loan | 01010110058678000 | ||
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مخـــزن الرســائل الجـــامعية - البدروم | المكتبة المركزبة الجديدة - جامعة القاهرة | Cai01.20.03.M.Sc.2012.Ab.U (Browse shelf(Opens below)) | 58678.CD | Not for loan | 01020110058678000 |
Thesis (M.Sc.) - Cairo University - Faculty of Computers and Information - Department of Computer Science
Egypt has a heavy burden of HCV infection (10%). The aim of this work is to develop a series of predictive models that can predict patients who are likely to respond to optimize the cost of treatment. This study included 2962 HCV patient. Data cleansing, auditing and feature selection were done. We constructed three decision tree learning algorithms-C4.5, CART, FDTL
Issued also as CD
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