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. 2021 Nov 6:2021:8336887.
doi: 10.1155/2021/8336887. eCollection 2021.

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

Lijian Ren  1 , Xiaoyu Wu  2 , Kaiqing Zhao  1
Affiliations

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

Lijian Ren et al. Comput Intell Neurosci. .

Retraction in

Abstract

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.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Structure diagram of the obesity monitoring system.
Figure 2
Figure 2
Structure of the biological neuron.
Figure 3
Figure 3
Structure diagram of the BP neural network.
Figure 4
Figure 4
Training process of the SA-BP neural network.
Figure 5
Figure 5
TMEM18 genes and disease networks.
Figure 6
Figure 6
Feature subset redundancy rate.
Figure 7
Figure 7
Performance comparison between the SA-BP neural network and BP neural network.
Figure 8
Figure 8
Prediction accuracy of the BP neural network and SA-BP neural network.
Figure 9
Figure 9
Output error of the obesity monitoring model.
Figure 10
Figure 10
Monitoring accuracy of the obesity monitoring model.
See this image and copyright information in PMC

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