A comparative study of decision Tree and Naïve Bayesian Classifiers on Verbal Autopsy Datasets
With an increased effort to reduce mortality rate in most developing countries, accurate information on the causes of such mortalities is a very crucial component for the development and formulation of health policy, strategies and other key critical decisions in the health sector. However there is lack of complete, accurate and reliable vital registration system that is expected to generate and report accurate causes of death information for health intervention policies and other programs. This research sets out to make a comparative evaluation of two most common supervised machine learning approaches Naive Bayes (NB) and J48 decision tree which builds a decision tree in the context and with the aid of Institute for Health Metrics and Evaluation (IHME) Verbal Autopsy (VA) dataset. This research also focuses on experimental comparison of these two state of art supervised learning techniquues with respect to their accuracy of correctly classified instances, incorrectly classified instances and very important Receiver Operating Characteric (ROC) Area which helps in understanding the classification model and their results, which can also help other researchers in making decision for the selection in classification model based on their data and number of attributes.