Open AccessBlood Pressure Classification Using the Method of the Modular Neural Networks
Author(s) -
Martha Pulido,
Patricia Melín,
German Prado-Arechiga
Publication year - 2019
Publication title -
international journal of hypertension
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.744
H-Index - 37
eISSN - 2090-0392
pISSN - 2090-0384
DOI - 10.1155/2019/7320365
Subject(s) - modular design , blood pressure , backpropagation , artificial neural network , medicine , modular neural network , pulse pressure , conjugate gradient method , artificial intelligence , pattern recognition (psychology) , computer science , algorithm , time delay neural network , operating system
In this paper, we present a new model based on modular neural networks (MNN) to classify a patient's blood pressure level (systolic and diastolic pressure and pulse). Tests are performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods. The modular neural network architecture is formed by three modules. In the first module we consider the diastolic pressure data; in the second module we use details of the systolic pressure; in the third module, pulse data is used and the response integration is performed with the average method. The goal is to design the best MNN architecture for achieving an accurate classification. The results of the model show that MNN presents an excellent classification for blood pressure. The contribution of this work is related to helping the cardiologist in providing a good diagnosis and patient treatment and allows the analysis of the behavior of blood pressure in relation to the corresponding diagnosis, in order to prevent heart disease.