Modelo híbrido para el diagnóstico de enfermedades cardiovasculares basado en inteligencia artificial

The present work pertains in the field of bioinformatics, particularly in the field data mining using Bayesian networks and decision trees. The study also assesses the usefulness of a Bayesian methodology when making medical predictions and diagnosis of non-trivial diseases (cardiovascular). Bayesian networks are used as graphic representations of previous knowledge, and also reasoning methods are applied to probabilistic models. When classifying the data from the database, problems still arise, thus the structure obtained might exhibit an unnecessary complexity degree which makes it difficult to represent and interpret knowledge as well as reducing efficiency in the inference process.