## 1 Introduction  
Currently the use of intelligent computing techniques in medicine is becoming more common, and some of them are: neural networks, fuzzy logic and evolutionary computation [1; 2; 3; 4; 5; 6; 7]. The main idea in this paper is to obtain a fuzzy neural hybrid model that provides a fast and accurate diagnosis and therefore it is necessary to have a 24 h patient monitoring database. Another aim is to have a modular neural network architecture, which will help us to have a precise modeling of the blood pressure trend of a patient, and also a fuzzy classifier is needed, which will classify in which level of blood pressure the patient is analyzed [8; 9; 10; 11; 12; 13; 14]. In this paper, we focus on the design of the fuzzy classifier to classify the blood pressure level. The output information by the modular neural network, in this case are systolic pressure and diastolic pressure that are used as inputs to the fuzzy classifier. It is expected that this proposed classifier can provide a faster, less expensive and more accurate results [15]. It is important to know that at present there are few works done using intelligent computer techniques for the diagnosis of blood pressure and in most of these works they carry out the classification of a general way with low, medium and high levels and we use hypotension, Optimal, normal, high normal, hypertension grade 1, 2, 3 and isolated systolic hypertension grade 1, 2, 3 as levels based on Table 1 from the guidelines of European Society of Cardiology. Some research works have been done to diagnose blood pressure using intelligent techniques, for example the work of Hypertension Diagnosis using a Fuzzy Expert System [16], in this work a fuzzy expert system is created for evaluate the risk of hypertension in a patient given risk factors and blood pressure, in this case the expert system does not give the classification values of blood pressure only the risk. In another work, a Genetic Neuro Fuzzy System for Hypertension Diagnosis [17] uses a genetic algorithm to initialize the Neuro Fuzzy System using a backpropagation network, and the systems diagnosis only the risk of hypertension but not the classification levels. A third work is a neural network expert system for diagnosing and treating hypertension [3], in this work a Model of Neural Network was created for diagnosis and treated of hypertension constructing models of \"Hypernet\" using as expert system, and differs as only is an expert system for diagnosis, not for classification. All these works differ in that they only use the intelligent techniques to evaluate the risk diagnosis of hypertension using risk factors and a one measurement of blood pressure. In this work we construct a complete model for classification of hypertension based on the trend of blood pressure of the patient given by the neural network using the reads of 24 h of blood pressure monitoring. The implementation of an appropriate method for modeling this problem has always been a major concern for the physician that aims at achieving the best possible diagnosis for the patient. Hypertension diagnosis is a very important problem in medicine. Hypertension is a dangerous disease that seriously threatens the health of persons. This disease often leads to fatal results, such as: heart attack, cerebrovascular accident and renal insufficiency. The main dangerous aspect of hypertension is that the persons may not know they have this disease, about one-third of the people with high blood pressure do not know it. Regular checkups, is the only way to know if the blood pressure is high. Nowadays in medicine is very common to use modeling approaches applied to diagnose some future illness and to treat them in the best way. The reason of the implementation of an appropriate method of modeling has always been an important topic in medicine, because it helps to treat diseases in time and save lives.