Application of Data Mining techniques to relate Cardiovascular Risk and Coronary Calcium

Abstract

Introduction: Knowledge Discovery in Databases (KDD) constitutes a process that allows data sets to be modeled and analyzed in an automated and exploratory manner. In this sense, data mining can be considered the main core of this procedure. Objective: In this study, a classification of clinical subjects (cluster) based on the comparison of parameters associated to cardiovascular risk factors was performed by means of KDD-based algorithms. Materials and Methods: the K-means algorithm, Hierarchical Agglomerative Clustering and Kohonen's Self-organizing Maps were applied to the database in order to obtain relationships based on the dissimilarity of its constitutive fields. Results: Four different clusters were obtained, represented by a group of well-defined clustering rules. Conclusion: KDD can be used to extract relevant data from clinical databases, which are strongly correlated with well-known cardiovascular risk markers.