Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm

With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slo...

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Autores principales:	Lijian Ren, Xiaoyu Wu, Kaiqing Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/a7e29b2f3e0c4702890fccd446e557ff
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spelling	oai:doaj.org-article:a7e29b2f3e0c4702890fccd446e557ff2021-11-15T01:19:08ZObesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm1687-527310.1155/2021/8336887https://doaj.org/article/a7e29b2f3e0c4702890fccd446e557ff2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/8336887https://doaj.org/toc/1687-5273With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.Lijian RenXiaoyu WuKaiqing ZhaoHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Lijian Ren Xiaoyu Wu Kaiqing Zhao Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
description	With the rapid development of information technology, hospital informatization has become the general trend. In this context, disease monitoring based on medical big data has been proposed and has aroused widespread concern. In order to overcome the shortcomings of the BP neural network, such as slow convergence speed and easy to fall into local extremum, simulated annealing algorithm is used to optimize the BP neural network and high-order simulated annealing neural network algorithm is constructed. After screening the potential target indicators using the random forest algorithm, based on medical big data, the experiment uses high-order simulated annealing neural network algorithm to establish the obesity monitoring model to realize obesity monitoring and prevention. The results show that the training times of the SA-BP neural network are 1480 times lower than those of the BP neural network, and the mean square error of the SA-BP neural network is 3.43 times lower than that of the BP neural network. The MAE of the SA-BP neural network is 1.81 times lower than that of the BP neural network, and the average output error of the obesity monitoring model is about 2.35 at each temperature. After training, the average accuracy of the obesity monitoring model was 98.7%. The above results show that the obesity monitoring model based on medical big data can effectively complete the monitoring of obesity and has a certain contribution to the diagnosis, treatment, and early warning of obesity.
format	article
author	Lijian Ren Xiaoyu Wu Kaiqing Zhao
author_facet	Lijian Ren Xiaoyu Wu Kaiqing Zhao
author_sort	Lijian Ren
title	Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
title_short	Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
title_full	Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
title_fullStr	Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
title_full_unstemmed	Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm
title_sort	obesity mass monitoring in medical big data based on high-order simulated annealing neural network algorithm
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/a7e29b2f3e0c4702890fccd446e557ff
work_keys_str_mv	AT lijianren obesitymassmonitoringinmedicalbigdatabasedonhighordersimulatedannealingneuralnetworkalgorithm AT xiaoyuwu obesitymassmonitoringinmedicalbigdatabasedonhighordersimulatedannealingneuralnetworkalgorithm AT kaiqingzhao obesitymassmonitoringinmedicalbigdatabasedonhighordersimulatedannealingneuralnetworkalgorithm
_version_	1718429001916088320

Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm

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