This article suggests a generalized method of discrimination which extends the classical Discriminating Factorial Analysis (FDA) to symbolic objects. This method is based on the adaptation of the classical Bayesian rule of discrimination to symbolic objects. This adaptation is done taking into account various elements, namely: a certain « density » measure on the observation space of symbolic objects; discriminant functions giving an idea of the « similarity » which exists between an observation and the individuals of the formation whole. This rule depends on the formation data and is typically built of the in view the minimization of the overall error rate.The purpose of this study is to solve a capital medical problem. Indeed, several cases of sudden deaths are noted these last years in the whole world and more particularly in our country the Democratic Republic of Congo (RDC), due to Cerebral Vascular Accidents (CVA) or acute coronary events (Heart attacks). The evolution and the prevailence of these cardiovascular diseases present a certain number of real and urgent problems to policy makers and other medical officials. Many undertaken epidemiologic studies these 20 last years led to the identification of the principal Factors of cardiovascular risk (FCVR), opening the way to preventive treatment. It is on the basis of these factors of risk that we designed a tool of decision-making aid medical.This generalized method of discrimination therefore makes it possible to produce decisions concerning whether or not a data point belongs to a predefined class, by using formation sets, from an assigning algorithm of the symbolic objects to classes that we suggest here.