Abstract:
Hepatitis C is a viral infection that causes liver inflammation, sometimes leading to
serious liver damage. Globally, an estimated 71 million people have chronic Hepatitis
C virus infection and, although research in the area is ongoing, there is currently no
effective vaccine against Hepatitis C. It has also been noted that the poor prediction of
hepatitis at various health institutions has also led to mass infection, hence this study.
The aim of this study is to develop a model that will aid medical experts and novice
alike in the classification of the survival of patients with hepatitis C virus (HCV) under
treatment so as to mitigate the onset of untimely death based on information assessed
from patients with HCV.
In order to achieve the aims and objectives identified for this study, C4.5 Decision Trees
Algorithm was used to formulate the prediction model for the survival of Hepatitis
Disease based on the data collected The model was simulated using a Waikato
Environment for Knowledge Analysis (WEKA) software and The model was validated
based on accuracy, sensitivity, false alarm rate and precision using the data collected.
The classification model developed in this study can be integrated into health
information Systems in order to complement electronic health records systems which
collect information about the identified variables and can be processed by the
classification model for the identification of the clinical outcome of patients to whom
treatment is provided.
