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COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty

Author

Listed:
  • Erwin Cornelius

    (Department of Mathematics, Illinois State University, Normal, IL 61701, USA)

  • Olcay Akman

    (Department of Mathematics, Illinois State University, Normal, IL 61701, USA)

  • Dan Hrozencik

    (Department of Mathematics, Chicago State University, Chicago, IL 60628, USA)

Abstract

The abundance of type and quantity of available data in the healthcare field has led many to utilize machine learning approaches to keep up with this influx of data. Data pertaining to COVID-19 is an area of recent interest. The widespread influence of the virus across the United States creates an obvious need to identify groups of individuals that are at an increased risk of mortality from the virus. We propose a so-called clustered random forest approach to predict COVID-19 patient mortality. We use this approach to examine the hidden heterogeneity of patient frailty by examining demographic information for COVID-19 patients. We find that our clustered random forest approach attains predictive performance comparable to other published methods. We also find that follow-up analysis with neural network modeling and k-means clustering provide insight into the type and magnitude of mortality risks associated with COVID-19.

Suggested Citation

  • Erwin Cornelius & Olcay Akman & Dan Hrozencik, 2021. "COVID-19 Mortality Prediction Using Machine Learning-Integrated Random Forest Algorithm under Varying Patient Frailty," Mathematics, MDPI, vol. 9(17), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:17:p:2043-:d:621399
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    References listed on IDEAS

    as
    1. Alfaro, Esteban & Gamez, Matias & García, Noelia, 2013. "adabag: An R Package for Classification with Boosting and Bagging," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 54(i02).
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