Heart Disease Risk Prediction Model (hdrpm) Methodology for Diabetic Patients

In the first experiment, presented Naïve Bayes data mining classifier technique has been applied which produces an optimal prediction model using minimum training set to predict the chances of diabetic patient getting heart disease. The diagnosis of diseases plays vital role in medical field. Using diabetic’s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease. It should be noted that the attributes used in our proposed method are those used for diagnosis of diabetes and are not direct indicators of heart disease.