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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (8): 122-133    DOI: 10.11925/infotech.2096-3467.2021.1269
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Predicting Major Infectious Diseases Based on Grey Wolf Optimization and Multi-machine Learning: Case Study of COVID-19
Qu Zongxi,Sha Yongzhong(),Li Yutong
School of Management, Lanzhou University, Lanzhou 730099, China,Research Center for Emergency Management, Lanzhou University, Lanzhou 730099, China
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Abstract  

[Objective] This paper tries to build an accurate and effective forecasting model for major infectious diseases based on multi-machine learning, aiming to predict outbreak trends and help formulate countermeasures in advance. [Methods] We established an ensemble prediction model with three machine learning optimal weight combinations of ANFIS, LSSVM and LSTM from the Gray Wolf Optimization algorithm. Then, we assessed the model’s prediction performance with the COVID-19 epidemic data. [Results] The ANFIS, LSSVM, and LSTM were suitable for predicting confirmed cases, death cases, and recovery cases. The average R2 of the proposed model reached 0.989, 0.993 and 0.987for the three scenarios. The average RMSE were 37.37%, 63.93% and 53.37% lower than the single model, respectively. [Limitations] The model needs to be examined with data sets on other major infectious diseases. [Conclusions] The ensemble prediction model based on Gray Wolf Optimization can effectively merge the advantages of multiple machine learning models to obtain stable and accurate results.
Key words： Major Infectious Disease Outbreak      Ensemble Prediction      Grey Wolf Optimization      Machine Learning     
Received: 07 November 2021      Published: 23 September 2022
ZTFLH:  R183  
  TP181  
Fund:National Natural Science Foundation of China(72004086)
Corresponding Authors: Sha Yongzhong,ORCID： 0000-0002-2479-2335     E-mail: shayzh@lzu.edu.cn
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Zongxi Qu
Yongzhong Sha
Yutong Li

Cite this article:

Qu Zongxi, Sha Yongzhong, Li Yutong. Predicting Major Infectious Diseases Based on Grey Wolf Optimization and Multi-machine Learning: Case Study of COVID-19. Data Analysis and Knowledge Discovery, 2022, 6(8): 122-133.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.1269     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I8/122

Grey Wolf Hunting
评价准则 计算公式
RMSE 1 N ∑ n = 1 N ( y n - y ^ n ) 2 1 / 2
MAE 1 N ∑ n = 1 N y n - y ^ n
R2 1 - ∑ n = 1 N ( y n - y ^ n ) ∑ n = 1 N ( y n - y - )
Model Performance Evaluation Criteria
模型 实验参数 参数值
ANFIS FIS Generation Method ‘FCM’
Partition matrix exponent 2
Number of clusters 10
Maximum number of epochs 200
LSSVM Kernel ‘RBF_kernel’
Type ‘function estimation’
Maximum number of epochs 200
LSTM NumFeatures 1
NumResponses 1
NumHiddenUnits 100
Maximum number of epochs 200
Machine Learning Model Parameter Setting
Comparison of Prediction Errors of Different Machine Learning Models
国家 病例种类 权重系数
ANFIS LSSVM LSTM
巴西 确诊病例 0.433 7 0.229 3 0.336 1
死亡病例 0.258 6 0.633 7 0.106 9
恢复病例 0.127 8 0.169 3 0.702 9
德国 确诊病例 0.775 5 0.192 2 0.033 9
死亡病例 0.021 2 0.904 3 0.077 3
恢复病例 0.854 5 0.008 1 0.140 0
印度 确诊病例 0.959 8 0.000 8 0.039 7
死亡病例 0.386 3 0.360 7 0.252 8
恢复病例 0.021 3 0.236 0 0.743 4
The Weight Coefficients of the Ensemble Model Obtained by GWO Optimization
算法 误差指标 确诊病例 死亡病例 恢复病例
ANFIS RMSE 61 907.389 9 067.774 51 677.476
MAE 53 346.317 7 823.019 41 125.250
R2 0.988 0.670 0.992
LSSVM RMSE 102 874.191 3 519.665 54 504.770
MAE 93 877.450 3 138.636 44 329.653
R2 0.966 0.950 0.992
LSTM RMSE 65 036.197 4 691.670 44 754.954
MAE 57 760.524 4 207.787 31 966.607
R2 0.986 0.912 0.995
平均集合 RMSE 38 347.996 543.311 45 580.268
MAE 35 297.435 452.753 36 317.756
R2 0.995 0.999 0.994
GWO优化集合 RMSE 19 004.084 351.125 43 304.957
MAE 13 251.499 300.861 33 187.829
R2 0.999 1.000 0.995
Comparison of Prediction Errors of the Models in Brazil
算法 误差指标 确诊病例 死亡病例 恢复病例
ANFIS RMSE 2 570.413 36.020 3 604.251
MAE 1 464.941 21.973 2 326.934
R2 0.966 0.988 0.912
LSSVM RMSE 3 636.940 36.004 5 095.082
MAE 2 541.193 28.337 4 563.184
R2 0.933 0.988 0.824
LSTM RMSE 3 833.311 68.183 2 748.320
MAE 2 490.947 63.268 1 951.193
R2 0.925 0.958 0.949
平均集合 RMSE 3 222.102 37.925 3 491.498
MAE 2 030.421 30.370 2 801.723
R2 0.947 0.987 0.917
GWO优化集合 RMSE 2 379.995 28.624 2 003.162
MAE 1 516.312 19.324 1 468.277
R2 0.971 0.993 0.973
Comparison of Prediction Errors of the Models in Germany
算法 误差指标 确诊病例 死亡病例 恢复病例
ANFIS RMSE 53 816.132 5 749.633 65 452.553
MAE 44 922.194 5 224.422 53 986.358
R2 0.988 0.702 0.986
LSSVM RMSE 56 798.467 1 670.177 375 812.555
MAE 44 066.724 1 233.361 293 712.684
R2 0.987 0.975 0.540
LSTM RMSE 189 399.641 2 202.258 245 591.605
MAE 165 611.399 1 997.203 214 948.692
R2 0.853 0.956 0.804
平均集合 RMSE 99 469.679 2 380.371 228 009.859
MAE 84 628.157 1 779.095 187 542.846
R2 0.960 0.949 0.831
GWO优化集合 RMSE 32 503.480 1 187.918 48 544.028
MAE 26 845.140 980.660 41 433.556
R2 0.996 0.987 0.992
Comparison of Prediction Errors of the Models in India
Predicted and Actual Values of Different Models in Brazil
Predicted and Actual Values of Different Models in Germany
Predicted and Actual Values of Different Models in India
国家 模型 DM
确诊病例 死亡病例 恢复病例
巴西 ANFIS 5.945 7.130 1.930
LSSVM 8.588 7.583 2.273
LSTM 6.746 8.539 0.445
平均集合 5.364 3.373 0.976
GWO优化集合 - - -
德国 ANFIS 1.384 1.690 1.501
LSSVM 2.859 2.673 6.573
LSTM 2.683 7.020 2.974
平均集合 2.212 2.898 3.472
GWO优化集合 - - -
印度 ANFIS 4.469 7.950 3.204
LSSVM 3.869 3.096 5.108
LSTM 6.614 4.783 6.648
平均集合 5.826 4.454 5.571
GWO优化集合 - - -
Results of DM Test
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