Decision Making for Diseases by using Bayesian Classifier

Abstract

Classification is a form of data analysis that can be used to extract models describing important data classes or to predict future data trends. In Classification techniques, Naïve Bayesian Classifier is one of the simplest probabilistic classifiers. Bayesian Classifier is based on Bayes Theorem. Bayesian Classifiers are useful in predicting the probability that a sample belongs to a particular class. This paper studies the Naïve Bayesian Classifier and to classify class label of diseases data using Naïve Bayesian Classifier. This paper focuses on tooth care dataset and decides that tooth is care or prevention or care and prevention. This example of dataset contains 2160 instance and 8 attributes from UCI machine learning repository.