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• W3136378322 abstract "The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. In this work, a large COVID-19 data set consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1241 areas from December 18, 2019, to September 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. We proposed a machine learning model for COVID-19 prediction based on the broad learning system (BLS). Here, we leveraged random forest (RF) to screen out the key features. Then, we combine the bagging strategy and BLS to develop a random-forest-bagging BLS (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. In addition, we compared the forecasting results with linear regression (LR) model, [Formula: see text]-nearest neighbors (KNN), decision tree (DT), adaptive boosting (Ada), RF, gradient boosting DT (GBDT), support vector regression (SVR), extra trees (ETs) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and BLS.The RF-Bagging BLS model showed better forecasting performance in terms of relative mean-square error (RMSE), coefficient of determination ([Formula: see text]), adjusted coefficient of determination ([Formula: see text]), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Hence, the proposed model demonstrates superior predictive power over other benchmark models." @default.
• W3136378322 created "2021-03-29" @default.
• W3136378322 creator A5043260804 @default.
• W3136378322 creator A5054304362 @default.
• W3136378322 creator A5064571154 @default.
• W3136378322 creator A5088291390 @default.
• W3136378322 date "2021-11-01" @default.
• W3136378322 modified "2023-10-17" @default.
• W3136378322 title "Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic" @default.
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• W3136378322 doi "https://doi.org/10.1109/jiot.2021.3066575" @default.
• W3136378322 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35582242" @default.
• W3136378322 hasPublicationYear "2021" @default.
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