随机森林和SARIMA模型预测我国布鲁氏菌病发病率效果研究

Comparison of random forests and SARIMA in Predicting Brucellosis Incidence

目的对比研究随机森林(Random Forest,RF)和季节性差分自回归滑动平均模型(Seasonal Autoregressive Integrated Moving Average,SARIMA)两种模型预测布鲁氏菌病发病率的效果。方法利用2005—2017年中国疾病预防控制信息系统中报告的布鲁氏菌病病例,分别建立随机森林和SARIMA两种模型进行训练和预测,并比较两种模型的预测结果指标值。结果SARIMA模型和随机森林模型预测结果的R^(2)(R Squared)和均方根误差RMSE(Root Mean Squared Error)分别是0.904、0.034351和0.927、0.03345。结论两种模型的预测精度都较高,均能够预测我国布鲁氏菌病的发病率,随机森林预测效果略优于SARIMA模型,更具有实用价值。

Objective To compare the effects of random forest and SARIMA(Seasonal Autoregressive Integrated Moving Average)on predicting incidence rate of brucellosis.Methods Using Brucellosis cases reported in the China Disease Prevention and Control Information System from 2005 to 2017,two models,random forest and SARIMA,were established for training and forecasting,and the forecasting results of the two models were compared.Results The R^(2)(R Squared)and RMSE(Root Mean Squared Error)of SARIMA model and random forest model are 0.904,0.034351,0.927 and 0.03345 respectively.Conclusion Both models have high prediction accuracy and can predict the incidence of brucellosis.Random forest prediction is a little bit better than SARIMA model and has more practical value.