Most existing classification algorithms for cardiovascular disease are limited to specific diseases and cannot categorize the severity of the diseases. These algorithms still need to be improved in terms of accuracy and generalizability. Therefore, a hybrid Improved Brain Storm Optimization with Support Vector Machine (IBSO-SVM) for cardiovascular disease classification is proposed. In this study, a knowledge-driven intelligent initialization method is proposed to enhance the optimization capability of IBSO and the accuracy of IBSO-SVM. Experimental evaluations are conducted on multiple real-world datasets, and the results demonstrate the superior performance of IBSO-SVM in cardiac disease datasets. The accuracy of BSO-SVM reaches 100% on the Heart Failure and Heart Disease datasets, and the accuracy of IBSO-SVM reaches 99% on the Stroke dataset and 88% on the Cardiovascular disease dataset.