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Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML
Marques,Joao Alexandre Lobo1; Gois,Francisco Nauber Bernardo2; Xavier-Neto,José3; Fong,Simon James4
2021
Source PublicationSpringerBriefs in Applied Sciences and Technology
Pages69-87
AbstractThe use of computational intelligence techniques is being considered for a vast number of applications not only because of its increasing popularity but also because the results achieve good performance and are promising to keep improving. In this chapter, we present the basic theoretical aspects and assumptions of the LSTM model and H20 AutoML framework. It is evaluated on the prediction of the COVID-19 epidemiological data series for five different countries (China, United States, Brazil, Italy, and Singapore), each of them with specific curves, which are results of policies and decisions during the pandemic spread. The discussion about the results is performed with the focus on three evaluation criteria: R Score, MAE, and MSE. Higher R Score was obtained when the sample time series was smoothly increasing or decreasing. The results obtained by the AutoML framework achieved a higher R Score and lower MAE and MSE when compared with LSTM and also with other techniques proposed in the book, such as ARIMA and Kalman predictor. The application of machine learning algorithm selector might be a promising candidate for a good predictor for epidemic time series.
DOI10.1007/978-3-030-61913-8_5
URLView the original
Language英语
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Document TypeBook chapter
CollectionUniversity of Macau
Corresponding AuthorMarques,Joao Alexandre Lobo
Affiliation1.Laboratory of Neuroapplications,University of Saint Joseph,Macao
2.Machine Learning Department,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
3.Government Intelligence Cell,Secretary of Health of the Government of the State of Ceara,Fortaleza,Brazil
4.Department of Computer and Information Science,University of Macau,Macao
Recommended Citation
GB/T 7714
Marques,Joao Alexandre Lobo,Gois,Francisco Nauber Bernardo,Xavier-Neto,José,et al. Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML,2021:69-87.
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