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基于反向传播神经网络的高血压诊断方法对公共卫生的可持续性研究。

Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health.

机构信息

TecNM, Campus Tuxtla Gutiérrez, Carretera Panamericana Kilometro 1080, Tuxtla Gutiérrez 29050, Chiapas, Mexico.

National Science and Technology Council (Conacyt), Department of Computer Science, National Institute for Astrophysics, Optics and Electronics, San Andrés Cholula 72840, Puebla, Mexico.

出版信息

Sensors (Basel). 2022 Jul 14;22(14):5272. doi: 10.3390/s22145272.

DOI:10.3390/s22145272
PMID:35890963
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9316039/
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.

摘要

本文提出了一种基于人群研究的用于高血压诊断的多层前馈神经网络的开发。为了开发这种架构，考虑了几个对确定高血压风险至关重要的生理因素；诊断系统可以提供一种通过传统手段不易确定的解决方案。所得结果表明，由于我们生活的社会环境，如经济、压力、吸烟、酗酒、吸毒、肥胖、糖尿病、缺乏运动等，影响当今人类健康状况的可持续性，导致高血压。基于神经网络的诊断系统的结果显示出 90%的有效性，从而从分析的生理数据中诊断高血压风险产生了很高的期望。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/75c670e67087/sensors-22-05272-g003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/c87aac4d86ed/sensors-22-05272-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/9946585e2f16/sensors-22-05272-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/40e2808cb516/sensors-22-05272-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/cba429a87359/sensors-22-05272-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/4dc77e812098/sensors-22-05272-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1086/9316039/2de5d962a198/sensors-22-05272-g012.jpg

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