Design of an Optimized Fuzzy Classifier for the Diagnosis of Blood Pressure with a New Computational Method for Expert Rule Optimization

Juan Carlos Guzman,German Prado-Arechiga,Patricia Melin

doi:10.3390/a10030079

Abstract

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.

Highlights

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]
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 providing an adequate modeling of the analyzed data for each patient
The following tables show the results obtained for 30 patients, and based on these results we obtain the classification accuracy rate and classification error rate, for which we use the following equations: The Classification Accuracy Rate (CA) is calculated as follows: CA =

Summary

Introduction

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 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 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]. 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

Methods

Results

Conclusion