收藏切换
Applications of CNN-LSTM model based on intelligent algorithm optimization in the prediction of hand, foot and mouth disease
收藏切换
PDF
Hao ZHOU1, 2, A-li DONG3, Hong LI3, Ya-nan KANG1, 2, Qi-yue YANG1, 2, Xing-yu WANG1, 2, Li-xia BAI1, 2, 4
Modern Preventive Medicine | 2024, 51(8) : 1364 - 1369
Less
收藏切换
Modern Preventive Medicine | 2024, 51(8): 1364-1369
Epidemiology and Statistical Methods Advances
Applications of CNN-LSTM model based on intelligent algorithm optimization in the prediction of hand, foot and mouth disease
Full
Hao ZHOU1, 2, A-li DONG3, Hong LI3, Ya-nan KANG1, 2, Qi-yue YANG1, 2, Xing-yu WANG1, 2, Li-xia BAI1, 2, 4
Affiliations
  • Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
Published: 2024-04-25 doi: 10.20043/j.cnki.MPM.202312044
Outline
收藏切换
Objective

Toanalyze the application of CNN-BiLSTM combination model and intelligent algorithm optimization in the prediction and early warning of HFMD incidence and to discussthe optimization model for predicting the incidence of HFMD, so as to provide reference for relevant departments to formulate prevention and control measures.

Methods

The monthly incidence data of hand, foot and mouth disease in Shanxi Province from January 2009 to December 2019 and the year-end resident population data released by the Shanxi Statistical Yearbook 2008-2020 were collected from January 2009 to December 2019.The monthly incidence data of hand, foot and mouth disease in Shanxi Province from January 2009 to December 2019 were used as sample modeling data to construct the corresponding models in MATLAB 7.6 software, and the prediction effect of each model was compared, and the optimal model was selected according to the principle that the smaller the error value and the higher the accuracy.

Results

By comparing the root mean square error and mean absolute error obtained by predicting the monthly incidence of foot and mouth disease of hand with different models, it can be seen that the CNN-BiLSTM model optimized based on intelligent algorithm is significantly better than the unoptimized CNN-BiLSTM combination model, that is, the values of RMSE and MAE of CNN-BiLSTM-PSO/GAPSO/SSA (1.943 3,1.309 7; 1.879 2, 1.240 2; 1.419 5, 1.169 1) is smaller than the corresponding CNN-BiLSTM model (2.066 3, 1.390 8), among which the CNN-BiLSTM-SSA combination model performs best.

Conclusion

The CNN-LSTM-SSA model has good predictive performance and accuracy in predicting the monthly incidence trend of HFMD, which can be used to predict the future incidence of HFMD in Shanxi Province.

HFMD  /  LSTM  /  CNN-LSTM  /  PSO  /  SSA  /  GAPSO
Hao ZHOU, A-li DONG, Hong LI, Ya-nan KANG, Qi-yue YANG, Xing-yu WANG, Li-xia BAI. Applications of CNN-LSTM model based on intelligent algorithm optimization in the prediction of hand, foot and mouth disease[J]. Modern Preventive Medicine, 2024 , 51 (8) : 1364 -1369 . DOI: 10.20043/j.cnki.MPM.202312044
Year 2024 volume 51 Issue 8
PDF
38
16
Cite this Article
BibTeX
Article Info
doi: 10.20043/j.cnki.MPM.202312044
  • Receive Date:2023-12-04
  • Online Date:2026-03-16
  • Published:2024-04-25
Article Data
Affiliations
History
  • Received:2023-12-04
Funding
Affiliations
    Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
References
Share
https://castjournals.cast.org.cn/joweb/xdyfyx/EN/10.20043/j.cnki.MPM.202312044
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT