Neural Network Backpropagation with Applications into Nutrition

Medina-Santiago, A.; Villegas, José M.; Ramirez-Torres, J.; García-Chong, N. R.; Cisneros-Gómez, A.; Melgar-Paniagua, E. M.; Bermudez-Rodriguez, J. I.

doi:10.1007/978-3-319-59397-5_6

Cited by 3 publications

(1 citation statement)

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“…Neural networks are a simulation of the biological diversity properties of neural systems by means of mathematical models recreated through artificial mechanisms (an integrated circuit or a computer) [ 5 , 16 , 17 ]. The aim is to have machines give responses similar to those that the human brain is capable of giving.…”

Section: Design Methodologymentioning

confidence: 99%

Hypertension Diagnosis with Backpropagation Neural Networks for Sustainability in Public Health

Torres

Medina-Santiago²,

Villegas

et al. 2022

Sensors

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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.

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Section: Design Methodologymentioning

confidence: 99%