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Volume 22
Issue 14
10.3390/s22145272
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Article

Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health

by
Jorge Antonio Orozco Torres
1,*,†,
Alejandro Medina Santiago
2,*,†,
José Manuel Villegas Izaguirre
3,4,
Monica Amador García
5 and
Alberto Delgado Hernández
3,4
1
TecNM, Campus Tuxtla Gutiérrez, Carretera Panamericana Kilometro 1080, Tuxtla Gutiérrez 29050, Chiapas, Mexico
2
National Science and Technology Council (Conacyt), Department of Computer Science, National Institute for Astrophysics, Optics and Electronics, San Andrés Cholula 72840, Puebla, Mexico
3
Facultad de Ciencias de la Ingeniería y Tecnología, Universidad Autónoma de Baja California, Boulevard Universitario #1000, Unidad Valle de las Palmas, Tijuana 21500, Baja California, Mexico
4
Center for Technological Research, Development and Innovation, University of Science and Technology Descartes, Tuxtla Gutiérrez 29065, Chiapas, Mexico
5
TecNM, Campus RioVerde, Carretera Rioverde-San Ciro Kilometro. 4.5, Rioverde 79610, San Luis Potosi, Mexico
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2022, 22(14), 5272; https://doi.org/10.3390/s22145272
Submission received: 30 May 2022 / Revised: 1 July 2022 / Accepted: 7 July 2022 / Published: 14 July 2022
(This article belongs to the Special Issue Digital Healthcare Leveraging Edge Computing and the Internet of Things)
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Abstract

This paper presents the development of a multilayer feed-forward neural network for the diagnosis of hypertension, based on a population-based study. For the development of this architecture, several physiological factors have been considered, which are vital to determining the risk of being hypertensive; a diagnostic system can offer a solution which is not easy to determine by conventional means. The results obtained demonstrate the sustainability of health conditions affecting humanity today as a consequence of the social environment in which we live, e.g., economics, stress, smoking, alcoholism, drug addiction, obesity, diabetes, physical inactivity, etc., which leads to hypertension. The results of the neural network-based diagnostic system show an effectiveness of 90%, thus generating a high expectation in diagnosing the risk of hypertension from the analyzed physiological data.
Keywords: backpropagation neuronal network; artery hypertension; health diagnosis; public health; sustainability backpropagation neuronal network; artery hypertension; health diagnosis; public health; sustainability

Share and Cite

MDPI and ACS Style

Orozco Torres, J.A.; Medina Santiago, A.; Villegas Izaguirre, J.M.; Amador García, M.; Delgado Hernández, A. Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health. Sensors 2022, 22, 5272. https://doi.org/10.3390/s22145272

AMA Style

Orozco Torres JA, Medina Santiago A, Villegas Izaguirre JM, Amador García M, Delgado Hernández A. Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health. Sensors. 2022; 22(14):5272. https://doi.org/10.3390/s22145272

Chicago/Turabian Style

Orozco Torres, Jorge Antonio, Alejandro Medina Santiago, José Manuel Villegas Izaguirre, Monica Amador García, and Alberto Delgado Hernández. 2022. "Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health" Sensors 22, no. 14: 5272. https://doi.org/10.3390/s22145272

APA Style

Orozco Torres, J. A., Medina Santiago, A., Villegas Izaguirre, J. M., Amador García, M., & Delgado Hernández, A. (2022). Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health. Sensors, 22(14), 5272. https://doi.org/10.3390/s22145272

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