Prognose coronary heart diseases through sphygmogram analysis and SVM classifier

A method of using statistical analysis on site-sampled sphygmogram data sets and support vector machines classifier to diagnose coronary heart disease is proposed. The hemodynamic parameters derived from sphygmogram reflect the status of human cardiovascular system. Based on homodynamic parameters, the dimension reduction methods and a modified support vector machines classifier are applied to meliorate prognosis sensitivity and specificity. The test results on clinical coronary heart disease patients show that this method has obvious advantages over existing classifier method in the captioned application.