Please wait a minute...
Data Analysis and Knowledge Discovery  0, Vol. Issue (): 1-    DOI: 10.11925/infotech.2020.0754
Current Issue | Archive | Adv Search |
Public Health Risk Forecasting With Multiple Machine Learning Methods Combined:Case Study of Influenza Forecasting in Lanzhou, China
Chai Guorong,Wang Bin,Sha Yongzhong
(School of Management, Lanzhou University, Lanzhou 730000, China)
(Hospital Management Research Center, Lanzhou University, Lanzhou 730000, China)
(Research Center for Emergency Management, Lanzhou University, Lanzhou 730000, China)
Export: BibTeX | EndNote (RIS)      

[Objective] This study tries to explore the practicability and effectiveness of forecasting public health risks with machine learning, taken influenza as an example. [Methods] First, we collected the data on influenza and meteorological factors during 2009 to 2016 in Lanzhou, China. Data from the year 2009 to 2015 were used as the training data and 2016 as the testing data. Then, based on SARIMA, Kalman Filter, and VAR, three machine learning methods for influenza prediction were put forward, respectively. Moreover, we designed two multi-method combined forecasting strategies. Finally, the forecasting performance of the above methods (strategies) was carefully evaluated and compared. [Results] The SARIMA, VAR, and Kalman Filter achieved best predict performance in the whole period (WP), outbreak period (OP), and stabilization period (SP), with RMSE at 11.68, 19.23, 1.60, and R2 at 0.932, 0.923, 0.956, respectively. The forecasting performance among all three scenarios was improved by our multi-method combined strategies, in which Comb_2 has better performance, with RMSE at 10.82, 14.68, 1.38, and R2 at 0.942, 0.934, 0.963, respectively. [Limitations] Limited by the data, this study just considered meteorology factors as external factors. [Conclusions] Predicting public health risks (such as influenza) with machine learning is practicable, effective and has great potential. But a lack of multi-source data is the major dilemma. Therefore, to promote the open exchange and sharing of data, barriers should be broken at the technical, organizational, and institutional levels.

Key words machine learning      influenza forecast      public health risk      risk forecast      
Published: 10 October 2020
ZTFLH:  C916  

Cite this article:

Chai Guorong, Wang Bin, Sha Yongzhong. Public Health Risk Forecasting With Multiple Machine Learning Methods Combined:Case Study of Influenza Forecasting in Lanzhou, China . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:     OR

[1] Wang Hanxue,Cui Wenjuan,Zhou Yuanchun,Du Yi. Identifying Pathogens of Foodborne Diseases with Machine Learning[J]. 数据分析与知识发现, 2021, 5(9): 54-62.
[2] Chen Donghua,Zhao Hongmei,Shang Xiaopu,Zhang Runtong. Optimizing Large Hospital Operating Rooms with Data Analytics[J]. 数据分析与知识发现, 2021, 5(9): 115-128.
[3] Che Hongxin,Wang Tong,Wang Wei. Comparing Prediction Models for Prostate Cancer[J]. 数据分析与知识发现, 2021, 5(9): 107-114.
[4] Su Qiang, Hou Xiaoli, Zou Ni. Predicting Surgical Infections Based on Machine Learning[J]. 数据分析与知识发现, 2021, 5(8): 65-75.
[5] Cao Rui,Liao Bin,Li Min,Sun Ruina. Predicting Prices and Analyzing Features of Online Short-Term Rentals Based on XGBoost[J]. 数据分析与知识发现, 2021, 5(6): 51-65.
[6] Zhong Jiawa,Liu Wei,Wang Sili,Yang Heng. Review of Methods and Applications of Text Sentiment Analysis[J]. 数据分析与知识发现, 2021, 5(6): 1-13.
[7] Xiang Zhuoyuan,Liu Zhicong,Wu Yu. Adaptive Recommendation Model Based on User Behaviors[J]. 数据分析与知识发现, 2021, 5(4): 103-114.
[8] Chai Guorong,Wang Bin,Sha Yongzhong. Public Health Risk Forecasting with Multiple Machine Learning Methods Combined:Case Study of Influenza Forecasting in Lanzhou, China[J]. 数据分析与知识发现, 2021, 5(1): 90-98.
[9] Chen Dong,Wang Jiandong,Li Huiying,Cai Sihang,Huang Qianqian,Yi Chengqi,Cao Pan. Forecasting Poultry Turnovers with Machine Learning and Multiple Factors[J]. 数据分析与知识发现, 2020, 4(7): 18-27.
[10] Liang Ye,Li Xiaoyuan,Xu Hang,Hu Yiran. CLOpin: A Cross-Lingual Knowledge Graph Framework for Public Opinion Analysis and Early Warning[J]. 数据分析与知识发现, 2020, 4(6): 1-14.
[11] Yang Heng,Wang Sili,Zhu Zhongming,Liu Wei,Wang Nan. Recommending Domain Knowledge Based on Parallel Collaborative Filtering Algorithm[J]. 数据分析与知识发现, 2020, 4(6): 15-21.
[12] Wang Shuyi,Liu Sai,Ma Zheng. Microblog Image Privacy Classification with Deep Transfer Learning[J]. 数据分析与知识发现, 2020, 4(10): 80-92.
[13] Ruojia Wang,Lu Zhang,Jimin Wang. Automatic Triage of Online Doctor Services Based on Machine Learning[J]. 数据分析与知识发现, 2019, 3(9): 88-97.
[14] Gang Li,Huayang Zhou,Jin Mao,Sijing Chen. Classifying Social Media Users with Machine Learning[J]. 数据分析与知识发现, 2019, 3(8): 1-9.
[15] Jiahui Hu,An Fang,Wanqing Zhao,Chenliu Yang,Huiling Ren. Annotating Chinese E-Medical Record for Knowledge Discovery[J]. 数据分析与知识发现, 2019, 3(7): 123-132.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938