Comparison of random forests and SARIMA in Predicting Brucellosis Incidence
10.3969/j.issn.1006-2483.2022.01.001
- VernacularTitle:随机森林和SARIMA模型预测我国布鲁氏菌病发病率效果研究
- Author:
Rui ZHANG
1
;
Xiaofeng WANG
2
;
Yewu ZHANG
2
;
Yanfei LI
3
Author Information
1. Chinese Center for Disease Control and Prevention, Beijing 102206, China
2. Chinese Center for Disease Control and Prevention, Beijing 102206, Chinaa
3. School of Health Sciences, Wuhan University, Wuhan 430071, China
- Publication Type:Journal Article
- Keywords:
Brucellosis;
Random Forest;
SARIMA;
ARIMA;
Machine learningA;
Incidence forecasting
- From:
Journal of Public Health and Preventive Medicine
2022;33(1):1-5
- CountryChina
- Language:Chinese
-
Abstract:
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 R2 (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.
