Research on Prediction of Medical Training Effect Based on PSO-BP Neural Network
10th International Conference on Orange Technology, ICOT 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2237327
ABSTRACT
Today, the world is still suffering from Coronavirus disease 2019(COVID-19) and other disasters. Therefore, it is critical to improve medical emergency professional training, and ensuring the training effect has become the top priority. As a result, this paper builds a Particle Swarm Optimization Back Propagation(PSO-BP) neural network model using training data from the National Disaster Life Support(NDLS) course to predict NDLS training outcomes. The PSO algorithm is used to calculate the initial weights of the BP network, and the model is then trained using error back propagation to obtain the predicted value of the training effect. When compared to the standard BP neural network prediction results, experimental analysis shows that the prediction model's accuracy reaches 93.24 percentage, and the prediction accuracy is improved by 11.71 percentage. It is also better in terms of convergence speed, minimum error, global search ability, and learning smoothness. This approach is suitable for medical training effect prediction and additionally to assist the training providers in grasping trainees' learning effects in advance to improve training quality. © 2022 IEEE.
BP neural network; effect prediction; medical training; PSO algorithm; Backpropagation; Disasters; Neural network models; Particle swarm optimization (PSO); Back-propagation neural networks; BP neural networks; Life supports; Optimization backs; Particle swarm; PSO algorithms; Swarm optimization; Training effects; Forecasting
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
/
Prognostic study
Language:
English
Journal:
10th International Conference on Orange Technology, ICOT 2022
Year:
2022
Document Type:
Article
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