A Hybrid Intelligent System Model for Hypertension Risk Diagnosis

Miramontes, Ivette; Martínez, Gonzalo; Melín, Patricia; Prado-Arechiga, German

doi:10.1007/978-3-319-67137-6_22

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…A particular modular neural network is utilized to give information to the classifier, this information is the systolic and diastolic weights and is given by every patient in the 24 h monitoring and this data are the inputs for the module of the modular neural network. In this phase, the neural network learns and models the data to finally give a result, which will be the inputs to the fuzzy system to classify in the most ideal way and provide a correct analysis and help the cardiologist to the precise control and diagnosis of each patient [31][32][33][34][35].…”

Section: Problem Statement and Proposed Methodsmentioning

confidence: 99%

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Guzman

Miramontes

Melín

et al. 2019

Axioms

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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.

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Section: Problem Statement and Proposed Methodsmentioning

confidence: 99%

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Guzman

Miramontes

Melín

et al. 2019

Axioms

Self Cite

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“…A hybrid neural model, described in [35][36][37], was previously designed for the same problem, using 3 fuzzy systems: the first provides the classification of the patient's blood pressure level [38][39][40], the second classifies the heart rate level and a third classifies the night profile of the patient [41]. Now, in this paper, we are using the Bird Swarm Algorithm for finding the optimal design of type-1 and type-2 fuzzy systems for the classification of the heart rate level of patients.…”

Section: Problem Statement and Proposed Methodsmentioning

confidence: 99%

Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

et al. 2018

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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.

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Optimization for Type-1 and Interval Type-2 Fuzzy Systems for the Classification of Blood Pressure Load Using Genetic Algorithms

Guzman

Melín

Prado-Arechiga³

2020

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications

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A Hybrid Intelligent System Model for Hypertension Risk Diagnosis

Cited by 6 publications

References 14 publications

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Optimal Design of Interval Type-2 Fuzzy Heart Rate Level Classification Systems Using the Bird Swarm Algorithm

Optimization for Type-1 and Interval Type-2 Fuzzy Systems for the Classification of Blood Pressure Load Using Genetic Algorithms

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