## 1 Introduction
The future data are modeled in medicine to help make decisions in the medium and long term due to the accuracy or inaccuracy of the modeled data and helps to have a better control in the health of the patient. For a physician, it is important to know the future behavior of blood pressure for a patient, since this allows having a better notion to make better decisions that improve the patient's health and avoid future problems, which can lead to a premature death, due to not having an adequate treatment to control blood pressure. We have previously considered a Neuro Fuzzy Hybrid Model for the diagnosis of blood pressure [15; 18; 19], in which experiments have been carried out with neural networks in order to obtain the best architecture of the network, in this case we were looking for the adequate number of Layers and the number of neurons per layer to have a good performance in the learning of the network and thus providing an adequate modeling of the analyzed data for each patient. Fuzzy logic has been used to make different classifiers [20; 21; 22; 23], of which we have been modifying: fuzzy rules and membership functions, with the aim of improving the classification of blood pressure levels. Finally, a graphical interface was also developed for data management by patient and thus to show the behavior of the monitoring of 24 h and the trend in visual graphics. The main contribution of this paper is the proposed optimized classifier, which is a part of the general model, which has several blocks like a database, modular neural network (MNN) and the fuzzy logic system (classifier). This classifier is applied to the diagnosis of blood pressure based on the European guidelines, which give us the different levels, in which we can classify blood pressure, the levels are: Hypotension, optimal, normal, normal high, hypertension grade 1, grade 2 hypertension, grade 3 hypertension, and finally we have isolated systolic hypertension which is also divided into grade 1 isolated systolic, grade 2 isolated systolic and grade 3 isolated systolic. This general model to which this classifier corresponds, consists of having a database of real patients, in this study we experimented with 30 patients, who were monitored during 24 h and during this period of time we obtained 45 samples per patient, these samples are the inputs for the MNN, which consists of two modules called systolic and diastolic. The MNN output is the trend per patient, this output will be the information that will be used as input in the classifier, which is optimized with bio-inspired techniques, and in this case we use genetic algorithms [24; 25]. The method of optimization consists of determining the number of necessary rules and changing the parameters of the membership functions and thus obtaining the best fuzzy classifier which will give us a correct diagnosis. After making different classifiers, two classifiers were finally obtained, which were compared, the first is the classifier based on an expert and the second is the optimized classifier, the two consist of two inputs that are the systolic and diastolic, have seven membership functions in each input and one output with ten membership functions, the classifiers are Mamdani-type, and the difference is that the expert-based classifier consists of 24 rules and the optimized classifier only has 21 rules. The optimized classifier is the one that gives us the best results so far with the tests carried out, which is why the main contribution of this work is the optimized classifier with genetic algorithms. This paper is organized as follows: In Section 2 the basic concepts are shown, in Section 3 the problem statement and the proposed method are presented, Section 4 shows the knowledge representation of the fuzzy system, Section 5 shows the simulation results of the proposed method, Section 6 presents the comparison of results and Section 7 offers the conclusions.