现代畜牧科技Issue(8)：27-33,7.DOI:10.19369/j.cnki.2095-9737.2024.08.006
基于长短时记忆模型的包虫病爆发风险预测混合模型的建立
Establishment of Risk Prediction Model for Echinococcosis Disease Outbreak based on Long Short-term Memory
摘要
Abstract
The aim of this study is to develop a hybrid model based on a time series decomposition method and a long short-term memory(LSTM)network to predict the risk of future outbreaks of infectious diseases such as baumatosis.Firstly,the incidence data of echinococcosis in China's provinces between 2004 and 2019 were obtained from the Scientific Data Centre of the National Ministry of Health of China.Secondly,a hybrid prediction model was then established by time series decomposition and LSTM network analysis.Finally,the accuracy of the prediction model was evaluated.The results showed that the hybrid model with trend components derived from time series decomposition combined with LSTM had a lower test error compared with the single LSTM model,indicating that the model has higher accuracy in incidence trends prediction.In conclusion,the hybrid model provides a reference and technical support for the incidence risk of encapsulated disease prediction with high accuracy,and provides a research basis for in-depth exploration of the interdisciplinary field combining machine learning and infectious diseases.关键词
包虫病/记忆/模型/风险/预测/机器学习Key words
echinococcosis/memory/model/risk/forecast/machine learning分类
医药卫生引用本文复制引用
陈春蓉,赵瑾,贺兆源,李家宝,陈海兰,贾耿介..基于长短时记忆模型的包虫病爆发风险预测混合模型的建立[J].现代畜牧科技,2024,(8):27-33,7.基金项目
巴马人才科技专项(巴人科20210034) （巴人科20210034）
