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Performance and explainability of feature selection-boosted tree-based classifiers for COVID-19 detection.

Jesús Rufino1, Juan Marcos Ramírez1, Jose Aguilar1,2,3

  • 1IMDEA Networks Institute, 28918, Madrid, Spain.

Heliyon
|January 3, 2024
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Summary

This study introduces a machine learning method using feature selection to accurately predict COVID-19 cases from self-reported data, enhancing public health surveillance. The approach considers diverse factors beyond symptoms for improved detection.

Keywords:
COVID-19 detectionExplainability analysisGradient boosting classifiersRandom forestRecursive feature eliminationShapley values

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Area of Science:

  • Computational epidemiology
  • Machine learning applications in public health
  • Infectious disease modeling

Background:

  • Self-reported data offers a vast resource for tracking infectious diseases like COVID-19.
  • Previous methods often used limited symptom sets, potentially missing broader influencing factors.
  • The University of Maryland Global COVID-19 Trends and Impact Survey (UMD-CTIS) collected extensive relevant information.

Purpose of the Study:

  • To develop and evaluate a machine learning methodology for predicting COVID-19-positive cases using comprehensive self-reported data.
  • To enhance the explainability and performance of predictive models through feature selection.
  • To identify key factors contributing to COVID-19 spread across different countries and time periods.

Main Methods:

  • Employed recursive feature elimination (RFE) for feature selection to optimize model input.
  • Utilized tree-based supervised machine learning classifiers: Random Forests (RF), Light Gradient Boosting (LGB), and Extreme Gradient Boosting (XGB).
  • Evaluated model performance using metrics like F1-score, sensitivity, specificity, precision, ROC, and AUC across Brazil, Canada, Japan, and South Africa for 2020-2021.

Main Results:

  • The methodology successfully predicted COVID-19 cases with high accuracy across diverse countries and timeframes.
  • Feature selection effectively reduced input variables without compromising detection performance.
  • Shapley values and feature importance analyses provided insights into the drivers of COVID-19 incidence.

Conclusions:

  • Machine learning models, enhanced by feature selection and broad data inputs, offer a robust approach for COVID-19 prediction.
  • The methodology demonstrates the utility of diverse data sources, including community information and vaccination status, for epidemiological surveillance.
  • Explainability analysis confirmed the model's ability to identify significant predictors of COVID-19 spread.