Using Naïve Bayes and Bayesian Network for Prediction of Potential Problematic Cases in Tuberculosis

Abstract

Both Data Mining techniques and Machine Learning algorithms are tools that can be used to provide beneficial support in constructing models that could effectively assist medical practitioners in making comprehensive decisions regarding potential problematic cases in Tuberculosis (TB). This study introduces two machine learning techniques which are Naïve Bayes inductive learning technique and the state of the art Bayesian Networks. These two techniques can be used towards constructing a model that can be used for predicting potential problematic cases in Tuberculosis. To construct a model, this study made have use of data collected from an Epidemiology laboratory. The volume of data was collated and divided into two data sets which are the training dataset and the investigation dataset. The model constructed by this study has shown a high predictive capability strength compared to other models presented on similar studies.