Research on the Prediction Model of Child Influenza Disease Based on Artificial Swarm Optimization Support Vector Machine
OBJECTIVE To use an improved artificial bee colony(ABC)optimization SVM prediction analysis model to improve SVM defects and improve the prediction accuracy of the model,so as to analyze and predict influenza diseases in children,providing reasonable data support for hospitals to better cope with the upcoming influenza season.METHODS Based on the 151days dose data of oseltamivir collected by children's hospital of Hebei province from 2022 to 2023,the first 101days were selected as training samples,and the remaining 50 days were used as test samples.MATLAB R2018b software was used to program and predict the data.The relative errors between the predicted data and the actual data were compared,and the determination coefficient and root mean square error were used mean absolute percentage error(MAPE)and mean absolute error(MAE)were compared and analyzed to verify the prediction accuracy of ABC-SVM.RESULTST he ABC-SVM model had the smallest indicators of error and the smallest relative error value with actual data,resulting in the best prediction effect.CONCLUSION This indicates that the ABC-SVM prediction model established in this article can accurately predict the arrival of the influenza season,which is of great significance for influenza control and prevention.
disease predictioninfluenza of childrensupport vector machineartificial bee colonyseasonal trend model
万方数据
维普
