Development of a Predictive Model for the Classification of the Survival of Hepatitis patients using Decision Trees Algorithm

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.