Intelligent Decision Framework to Explore and Control Infection of Hepatitis C Virus
Ali, Mohamed M. Reda
Khedr, Ayman E.
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This research presents Intelligent Decision Framework (IDF) to explore and manage cases of hepatitis c virus based on data mining approach and Fuzzy logic system. The proposed framework is produced from integration between data mining decision tree, rule based classification and fuzzy logic system. On the other hand, this study improves the predication results of Fibrosis stage by using Trapezoidal Fuzzy Number (TFN) distribution as fuzzy logical system to arrive 98.1% compared to predication results that were 92.5% by data mining decision tree model for same patients sample. Fuzzy logic system predicts disease scale of Hepatitis C Virus (HCV) for patients sample through different stages of liver disease caused by virus c. The proposed framework supports physicians and Ministry of Health (MOH) strategies for treatment to limit and control HCV infections and prevalence rate in Egypt and other countries. The extracted knowledge and information from proposed framework helps decision makers to take appropriate and better decision at appropriate time to against hepatitis c viral in world. The architecture of intelligent decision framework is designed to support physicians to investigate and present treatment for HCV cases. Also, to develop intelligent machine, health care system or robots as a physician for HCV patients in high prevalence rate countries.
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