Heart Disease Prediction using Naive Bayes Classification in Data Mining

In this research, data mining techniques will helpful to handle the predictive model. Research will show the most effective parameter of the heart disease prediction which gets the scenario for least predictive value and most predictive value from the dataset. 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. Results show that unique strength which identifies the objectives of the defined mining goal and expert system. 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. The System can discover and extract hidden knowledge associated with diseases (heart attack) from a historical heart disease database.