Blood Pressure Classification Using the Method of the Modular Neural Networks

International Journal of Hypertension
Martha PulidoGerman Prado-Arechiga

Abstract

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.

References

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Citations

Sep 20, 2019·The Journal of Clinical Hypertension·Prasanna Santhanam, Rexford S Ahima
Feb 23, 2021·Work : a Journal of Prevention, Assessment, and Rehabilitation·Mengqi ZhangV Sivakumar

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