Prediction of Cardiovascular Disease using Support Vector Machine

Cardiovascular is one of the major issues that have been facing in the world. The World Health Organization Statistics appeal is revealed that many of the people are suffering with cardiovascular disease. Millions of people die year after year because of cardiovascular disease. It is very important to diagnose earlier likewise we can minimize the people adversity from cardiovascular disease. Traditional way of diagnosis is not sufficient for such an illness. If cardiovascular disease could be predicted before, lots of patient deaths would be prevented and also a more accurate and efficient treatment way could be provided. Forecasting the Cardiovascular disease using knowledge engineering algorithms will provide a desirable and exact result when measured in traditional method. In this paper, prediction of cardiovascular disease method uses Support Vector Machine algorithm is recommended to designate the presence or lack of cardiovascular disease. 13 clinical attributes were liable as input for the neural network. The classification methods to take decision in the area of health care are namely Decision tree, Naive Bays, Neural Networks, Support Vector Machines and Random Forest. Using these techniques heart disease can be predicted accurately.