الفهرس 
Chapter 1: IntroductionOnly 14 pages are availabe for public view
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Abstract
A major objective of this thesis is to evaluate data mining tools in medical data to develop a tool that can help make accurate decisions. Therefore predictive model designed using data mining to detect HCV from Egyptian healthcare workers in Egypt HCV dataset that is capable of enhancing the reliability of HCV diagnosis.
The findings of this study revealed all the models built from Decision Trees classifier, Naïve Bayes classifier and Neural Network have high classification accuracy and are generally comparable in predicting HCV cases. However, comparison between three classifier accuracy in predicting HCV infection suggests that the Decision Trees model performs better and it is the most effective technique for detecting HCV infection for different dataset size as it has the highest percentage of correct predictions.
This study showed that data mining techniques can be used efficiently to model and predict patients infected with HCV or not.