Model construction for mortality trend of road traffic injury in Fujian Province
Objective To analyze the status of road traffic injury deaths in Fujian province from 2014 to 2021,and to explore the applicable trend prediction model.Methods Seasonal autoregressive integrated moving average(SARIMA),support vector regression(SVR)and long short-term memory(LSTM)network were constructed using road traffic injury deaths data from January 2014 to June 2021 in Fujian province to predict the mortality rate from July to December in 2021,and its prediction effects were evaluated by comparison with the actual value.Results The annual reporting rate of road traffic injuies in Fujian Province showed a decreasing trand from 2014 to 2021(AAPC=-6.29％,P＜0.001).LSTM network had highest prediction accuracy among three models,with root mean square error(RMSE)of 0.070 5,mean absolute error(MAE)of 0.061 2 and mean absolute percentage error(MAPE)of 8.72％.Conclusions The overall road traffic injury deaths in Fujian province showed a downward trend.LSTM network can be used to predict the short-term trend of road traffic injury deaths.
NETL
NSTL
万方数据
