Prediction of trend of premature birth in a hospital by the ARIMA model based on R language
FAN Jing-jie1, LIU Xue-fang1, LIU Shi-xin1, LIN Yi-cai1, MOU Jing-feng2
1. Shenzhen Maternal and Child Health Care Hospital, Shenzhen, Guangdong 518017, China; 2. Nanshan District Center for Disease Control and Prevention, Shenzhen, Guangdong 518054, China
Abstract：Objective To build a time series autoregressive integrated moving average (ARIMA) model of premature birth in a hospital, to predict the changing trend of premature birth in this hospital, and to provide a scientific basis for rational allocation of medical resources and policy formulation. Methods The number of daily cases concerning premature birth in Shenzhen Maternal and Child Health Care Hospital in 2016 was collected. R language was used to conduct time series analysis, ARIMA prediction model was constructed, and the prediction effect was evaluated. Results There were 1,738 cases of premature birth occurred in Shenzhen Maternal and Child Health Care Hospital in 2016, and the best prediction model was ARIMA (3,1,1). The Akaike Information Criterion of the ARIMA (3,1,1) was 1,680.67. Ljung-Box statistics value =0.16 was not significantly different (P=0.689), suggesting a white noise sequence of residuals with good model fitting. The average relative error between the predictive value and the actual value of ARIMA (3,1,1) was 9.2%, and the actual values were within 95%CI of the predictive values. Conclusions The ARIMA (3,1,1) model could forecast the changing trend of premature birth in Shenzhen Maternal and Child Health Care Hospital with good prediction effect.
樊静洁, 刘雪芳, 刘世新, 林一才, 牟敬锋. 基于R语言的ARIMA模型对医院早产趋势的预测分析[J]. 实用预防医学, 2020, 27(4): 429-432.
FAN Jing-jie, LIU Xue-fang, LIU Shi-xin, LIN Yi-cai, MOU Jing-feng. Prediction of trend of premature birth in a hospital by the ARIMA model based on R language. , 2020, 27(4): 429-432.
2020, Vol. 27 
