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  1. Home
  3. Random-forest-bagging Broad Learning System With Applications For Covid-19 Pandemic.
  1. Home
  3. Random-forest-bagging Broad Learning System With Applications For Covid-19 Pandemic.

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Random-Forest-Bagging Broad Learning System With Applications for COVID-19 Pandemic.

Choujun Zhan1,2, Yufan Zheng1, Haijun Zhang3

  • 1School of Electronical and Computer EngineeringNanfang College of Sun Yat-sen University Guangzhou 510970 China.

IEEE Internet of Things Journal
|May 18, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

A new machine learning model, random forest-bagging broad learning system (RF-Bagging-BLS), accurately forecasts COVID-19 pandemic trends. This advanced model outperforms existing methods in predicting the pandemic

Keywords:
Artificial intelligenceCOVID-19broad learning system (BLS)coronavirus disease 2019 (COVID-19) testing capacityrandom forest (RF)time-series forecasting

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Area of Science:

  • Epidemiology and Public Health
  • Data Science and Machine Learning
  • Computational Biology

Background:

  • The COVID-19 pandemic has caused a global health crisis, necessitating accurate forecasting.
  • Analysis and prediction of the COVID-19 pandemic have gained significant worldwide attention.
  • Existing forecasting models may not fully capture the complex dynamics of the pandemic.

Purpose of the Study:

  • To develop and evaluate a novel machine learning model for predicting COVID-19 pandemic trends.
  • To compare the performance of the proposed model against various established machine learning algorithms.
  • To leverage a comprehensive dataset encompassing pandemic, testing, economic, demographic, and geographic factors.

Main Methods:

  • A large dataset of COVID-19 information from 184 countries and 1241 areas (December 2019–September 2020) was compiled.
  • A random forest (RF) algorithm was used for feature selection.
  • A random-forest-bagging broad learning system (RF-Bagging-BLS) was developed and applied for forecasting.

    • Main Results:

    • The RF-Bagging-BLS model demonstrated superior forecasting performance compared to benchmark models.
    • Key performance metrics including RMSE, R-squared, adjusted R-squared, MAD, and MAPE indicated the model's accuracy.
    • The proposed model exhibited better predictive power than linear regression, KNN, decision tree, Ada, RF, GBDT, SVR, ETs, CatBoost, LightGBM, XGBoost, and BLS.

    Conclusions:

    • The RF-Bagging-BLS model offers a robust and accurate approach for COVID-19 pandemic forecasting.
    • This machine learning approach provides valuable insights for public health decision-making and resource allocation.
    • The study highlights the potential of advanced machine learning techniques in managing global health crises.