الفهرس 
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Abstract
Knowing the degree of a disease allows a physician to provide specific
treatment to the patient leading to better care. The use of serum markers
becomes important to predict liver fibrosis. The aim of this master’s thesis is to
predict liver fibrosis stage by constructing decision trees in patients with
chronic hepatitis C genotype 4 in Egypt. Also, we examined whether the
combination of certain biomarkers could increase the diagnostic accuracy of
liver fibrosis assessment. In addition, this study aims to evaluate whether
laboratory examinations can be used to determine the rate of liver fibrosis
progression in patients chronically infected with hepatitis C virus genotype 4.
This paper focuses on the decision tree classification technique which used to
build classification models in the form of a tree structure.
In this study, we included most previously known indirect markers of liver
fibrosis such as FibroTest which used to stage liver fibrosis to compare the
results of the non-invasive markers using histology as reference method. We
tried to develop a simple surrogate index comprised of routinely available
laboratory tests to reflect the histological fibrosis stage.
For the analysis, we used all the data for training as well as for testing as
follows: we used 88% as train data to develop the decision tree model and the
remaining designated 12% were used as test-data to predict Fibrosis Stage.
The results indicated that decision trees model was useful and effective to
predict liver fibrosis stage at least similar to biopsy and provide a qualitative
and quantitative overview for the physician to find the relations between
biomarkers panel and Liver Biopsy. Decision trees produced the correct
classification in approximately 94% ofthe cases.
Keywords: Biochemical Markers. Liver fibrosis. Hepatitis C virus. Data