The Prediction of New Medical Resources in China during COVID-19 Epidemic Period Based on Artificial Neural Network Model Optimized by Genetic Algorithm. Issue 1 (February 2021)

• Record Type: Journal Article
• Authors: Sun, Fumin; Su, Jie; Hu, Yong; Zhou, Ling
• Is Part Of: Journal of physics. Volume 1815:Issue 1(2021)
• Publication Date: 2021-02
• Subjects: Genetic algorithm -- Artificial neural network -- Optimization -- New crown infection number -- Medical resources; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1815/1/012033
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In this paper, a neural network model is constructed to predict the number of new infections, and on the basis of this model, the demand for medical resources of new infections is further predicted. Firstly, the traditional Back Propagation neural network is optimized by genetic algorithm, which solves the problem that Back Propagation neural network is easy to fall into the local optimal solution in the prediction. Taking the number of severe patients, the number of cured patients, the number of deaths, and the number of suspected patients on the first day of the new crown period as the input variables, and the number of newly diagnosed patients on the second day as the output variable, a new Back Propagation neural network is constructed Novel coronavirus pneumonia model is constructed. Secondly, according to the official documents on the new crown pneumonia research and the guide for the use of medical resources, the linear function of the infection proportion and the normal distribution function of each symptom duration are constructed. Finally, combined with the above two contents, a complete process is designed to achieve the medical treatment for the newly infected patients. Prediction of the demand for medical resources.