The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.

In this paper, the optimal designs of type-1 and interval type-2 fuzzy systems for the classification of the heart rate level are presented. The contribution of this work is a proposed approach for achieving the optimal design of interval type-2 fuzzy systems for the classification of the heart rate in patients. The fuzzy rule base was designed based on the knowledge of experts. Optimization of the membership functions of the fuzzy systems is done in order to improve the classification rate and provide a more accurate diagnosis, and for this goal the Bird Swarm Algorithm was used. Two different type-1 fuzzy systems are designed and optimized, the first one with trapezoidal membership functions and the second with Gaussian membership functions. Once the best type-1 fuzzy systems have been obtained, these are considered as a basis for designing the interval type-2 fuzzy systems, where the footprint of uncertainty was optimized to find the optimal representation of uncertainty. After performing different tests with patients and comparing the classification rate of each fuzzy system, it is concluded that fuzzy systems with Gaussian membership functions provide a better classification than those designed with trapezoidal membership functions. Additionally, tests were performed with the Crow Search Algorithm to carry out a performance comparison, with Bird Swarm Algorithm being the one with the best results.

A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The main goal is to model the behavior of blood pressure based on monitoring data of 24 h per patient and based on this to obtain the trend, which is classified using a fuzzy system based on rules provided by an expert, and these rules are optimized by a genetic algorithm to obtain the best possible number of rules for the classifier with the lowest classification error. Simulation results are presented to show the advantage of the proposed model.

Implement a fuzzy inference system for a nocturnal profile classification model, for knowing the nocturnal blood pressure behavior of the patients and with this gain knowledge of the current health condition of such patients, in order to prevent a cardiovascular event. A fuzzy system for the classification of nocturnal blood pressure is implemented, and this because the original tests that were performed with a traditional system of rules did not produced the desired results.The fuzzy system was empirically designed based on a medical article of nocturnal hypertension, in which the author defines that in order to obtain the profile of nocturnal blood pressure, the information of the ABPM obtained in the day and night should be separated. From which the quotient to obtain the night profile of patients is calculated, and from this the range of each membership function is obtained, which are those that provide us each level of classification. The fuzzy system has two inputs which are the quotient of the systolic pressure and the quotient of the diastolic pressure and as output the night profile of the patient. Tests were performed with a group of 200 patients, comparing the results of the traditional system rules with the fuzzy system, and having better results with the second, because the traditional system rules requires precise information. Tests are carried out with different architectures in which different types of membership functions were used, such as: trapezoidal and Gaussian, having better results with the fuzzy system with trapezoidal membership functions. A random sample of 30 patients was collected obtaining 90% of correct classification with the fuzzy system in comparison with the traditional system rule, which correctly classified 53.3% of the patients, with a standard deviation of 3.86. The table shows the comparison of the different experiments, being those marked with bold those that classified correctly. Fuzzy logic allows us to handle inaccurate information, which helps us obtain good results, which can be noted in the reported experiments, where this type of classification is more efficient than the traditional system rules.