Review of Heart Disease Prediction using Data Mining Classifications

In the research data mining techniques will helpful to handle this technique of predictive model. Research will show the most effective parameter of the heart disease prediction and will get the scenario for least predictive value and most predictive value in whole data mining technique. Initially need to identify the exact state of the user entering parameter which can be frequent item although random value of dataset in data modeling .This research has a prototype Heart Disease Prediction using data mining techniques, namely Naïve Bayes which in artificial Neural Network concept. Results show that each technique has its unique strength in identify the objectives of the defined mining goal and expert system .Researchers are using some medical exponent attributes such as age, sex, blood pressure and blood sugar , glucose and some related factors can predict the likelihood of patients getting a heart disease with its exact probability. Basically this technique is expended on the defected and non-defected parameter which works as result class.