7th International Conference on Data Mining & Knowledge Management Process (DKMP 2019)

July 13~14, 2019, Toronto, Canada

Accepted Papers


    Attribute Reduction And Decision Tree Pruning To Simplify Liver Fibrosis Prediction Algorithms
    Mahasen Mabrouk1, Abubakr Awad2, Hend Shousha1, Wafaa Alakel1,3, Ahmed Salama1, Tahany Awad1
    1Cairo University,Egypt, 2University of Aberdeen, Aberdeen,UK, 3National Hepatology and Tropical Medicine Research Institute, Egypt.
    ABSTRACT
    Assessment of liver fibrosis is a vital need for enabling therapeutic decisions and prognostic evaluations of chronic hepatitis. Liver biopsy is considered the gold standard for assessing the fibrosis stage but with several limitations, also FIB-4 and APRI have a limited accuracy. The “Egyptian National Committee for Control of Viral Hepatitis” has provided a rich pool of electronic data that data mining can explore to discover hidden patterns, trends and enables the development of predictive algorithms.
    KEYWORDS

    Liver Fibrosis, Data Mining, Weka, Decision Tree, Attribute Reduction, Tree Pruning.