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.
