reduce medical errors and improve health processes is a priority of all health personnel. in this context arise the "clinical support systems for decision making" (cdss), which are a key component in computerization of the clinical layer. with the evolution of technologies, large amounts of data have been studied and classified based on data mining. one of the main advantages of using this in the cdss, has been its ability to generate new knowledge. for this purpose, this paper presents, by combining two mathematical models, a way to contribute to the diagnosis of diseases using data mining techniques. hypertension was taken as a case study to show the models used. the research development methodology follows the most used processes of knowledge discovery in databases: crisp-dm 1.0, and relies on the free distribution tool weka 3.6.2. we obtained different patterns of behavior in relation to risk factors for developing hypertension using data mining techniques.