USO DE REDES BAYESIANAS OBTENIDAS MEDIANTE OPTIMIZACIóN DE ENJAMBRE DE PARTíCULAS PARA EL DIAGNóSTICO DE LA HIPERTENSIóN ARTERIAL

In the present work, different Artificial Intelligence techniques are combined to model the diagnosis of hypertensivepeople. To develop the work a data base of Arterial Hypertension was used, which is result of a preliminary studymade in five polyclinics of Santa Clara city, with supposedly healthy individuals. One of the ways to model therelations between variables is using a Bayesian network. The computational cost of the learning of a Bayesiannetwork from data, grows with the number of variables and the number of cases, therefore, the problem of identifyinga good heuristic to explore the space of possible networks arises. The evolutionary algorithms are being very valuablemethods to find good solutions to concrete problems, that is why the Particle Swarm Optimization (PSO) algorithm isused for the network structure search. An extension to the Weak platform (Waikato for Environment KnowledgeAnalysis) was done, in which the new algorithm becomes part of the global score metrics implemented in theBayesnet class in Weka. The obtained results show good classification of the Arterial Hypertension with Bayesiannetworks.