![]() Hence, the proposed model demonstrates superior predictive power over other benchmark models.Īrtificial intelligence COVID-19 broad learning system (BLS) coronavirus disease 2019 (COVID-19) testing capacity random forest (RF) time-series forecasting. The idea of random forests is to randomly select \(m\) out of \(p\) predictors as candidate variables for each split in each tree. In addition, we compared the forecasting results with linear regression (LR) model, -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 (), adjusted coefficient of determination (), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Random forest is an extension of Bagging, but it makes significant improvement in terms of prediction. 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. Here, we leveraged random forest (RF) to screen out the key features. We proposed a machine learning model for COVID-19 prediction based on the broad learning system (BLS). Random forest is a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of overcoming over-fitting. Random forests is a combination of bagging with random feature selection at each node. Compared to the standard CART model (Chapter ref (decision-tree-models)), the random forest provides a strong improvement, which consists of applying bagging to the data and. It is a special type of bagging applied to decision trees. Random forests provide an improvement over bagged trees by way of a. Random Forest algorithm, is one of the most commonly used and the most powerful machine learning techniques. 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. the Gini index decreases by splits over a given predictor averaged over all B trees. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. Introduction to Data Splitting Data into Training and Test sets Model 0: A Single Classification Tree Model 1: Bagging of ctrees Model 2: Random Forest for. The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis.
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