[Retracted] Obesity Mass Monitoring in Medical Big Data Based on High‐Order Simulated Annealing Neural Network Algorithm
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Post-publication record
- Nature
- Retraction
- Reason
- Concerns/Issues about Data;Concerns/Issues about Results and/or Conclusions;Concerns/Issues about Referencing/Attributions;Concerns/Issues about Peer Review;Investigation by Journal/Publisher;Investigation by Third Party;Computer-Aided Content or Computer-Generated Content;Unreliable Results and/or Conclusions;
- Date
- 6/28/2023 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
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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The record
- Venue
- Computational Intelligence and Neuroscience
- Topic
- Artificial Intelligence in Healthcare
- Field
- Health Professions
- Canadian institutions
- University of Toronto
- Funders
- Tianjin UniversityUniversity of Toronto
- Keywords
- Simulated annealingBig dataComputer scienceArtificial neural networkOrder (exchange)Data miningArtificial intelligenceAlgorithmBusinessFinance
- Has abstract in OpenAlex
- yes