Cardiovascular disease remains the biggest cause of deaths worldwide and the Heart Disease Prediction at the early stage is importance. In this paper Supervised Learning Algorithm is adopted for heart disease prediction at the early stage using the patient's medical record is proposed and the results are compared with the known supervised classifier Support Vector Machine (SVM). The information in the patient record is classified using a Cascaded Neural Network (CNN) classifier. In the classification stage 13 attributes are given as input to the CNN classifier to determine the risk of heart disease. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients. The results show the CNN classifier can predict the likelihood of patients with heart disease in a more efficient way.