In the recent years, the number of people passing away due to heart attack has increased a lot. The lifestyle changes of the 20th century have made people more prone to heart attacks. This paper presents a heart disease classification system using deep learning. The individual parameters causing the heart attack are analysed in terms of risk factor. The risk factor analysis has helped to access the role of each parameter at a personal patient level. The risk factor analysis has led to discovery of redundancy present in the datasets and thereby providing input on how accuracy can be increased. The proposed model used Convolution Neural Network (CNN) to classify the heart data. The UCI heart dataset is used to validate the proposed method. A custom dataset is constructed with new realtime parameters which has been recently discovered. The proposed method has achieved better accuracy when compared to the existing counterparts.