Using data mining technique to diagnosis heart disease

Medical diagnose is a promise application that exploits data mining techniques. The physicist diagnose, represented by human expertise, can be incurrence to fail. In contrast the data mining can recruit the extracted knowledge from huge of clinical data though data mining and produce a predictive model, use the classification task to achieve the diagnostic. Different methods exist in this field, to produce the classifier. One of them is naive bays. In this paper, we will present and discuss the experiment that was executed with naive bayes technique in order to built predictive model as an artificial diagnose for heart disease based on data set which contains set of parameters that were measured for individuals previously. Then compare the results with other techniques according to using the same data that were given from UCI repository data.