The Performance Comparison between Oral Cancer and Acute Inflammations Datasets Using Bayesian Model

Bayesian method is partial of intelligent computing methods that is using in reasoning and managing uncertainty problem. There is a growing interest in the use of Bayesian methods for medical diagnosis. In the literature, several studies have reviewed in different methods and findings for medical diagnosis. The purpose of this study is to compare oral cancer and acute inflammations dataset using Bayesian method for dieses diagnosis. The first experiment Bayesian method involved determining the different probabilities of the primary tumor stage as a function of demographics profile and risk habits. The second experiment involved determining the different probabilities of the acute inflammation diseases. From the experiments conducted, Bayesian method performs better on acute inflammation dataset compares to oral cancer dataset.