Cardiovascular disease is one of the leading causes of death in the world and the number of patients increases worldwide. To investigate the cardiovascular disease in early stage, reliable model predictions are required. In computational cardiovascular models parameters are one of the major sources of uncertainty, which make the models unreliable and less predictive. In order to achieve predictive models that allow the investigations of the cardiovascular diseases, sensitivity analysis (SA) can be used to quantify and reduce the output uncertainty caused by model parameters.