Machine learning algorithms for predicting COVID-19 mortality in Ethiopia.

Journal: BMC public health
Published Date: Jun 28, 2024

Abstract

BACKGROUND: Coronavirus disease 2019 (COVID-19), a global public health crisis, continues to pose challenges despite preventive measures. The daily rise in COVID-19 cases is concerning, and the testing process is both time-consuming and costly. While several models have been created to predict mortality in COVID-19 patients, only a few have shown sufficient accuracy. Machine learning algorithms offer a promising approach to data-driven prediction of clinical outcomes, surpassing traditional statistical modeling. Leveraging machine learning (ML) algorithms could potentially provide a solution for predicting mortality in hospitalized COVID-19 patients in Ethiopia. Therefore, the aim of this study is to develop and validate machine-learning models for accurately predicting mortality in COVID-19 hospitalized patients in Ethiopia.

Authors

  • Melsew Setegn Alie
    Department Public Health, School of Public Health, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia. melsewsetegn2010@gmail.com.
  • Yilkal Negesse
    Department of Public Health, College of Medicine and Health Science, Debre Markos University, Gojjam, Ethiopia.
  • Kassa Kindie
    Department Nursing, College of Medicine and Health Science, Mizan-Tepi University, Mizan-Aman, Ethiopia.
  • Dereje Senay Merawi
    Department of Information Technology, Faculty of Technology, Debre Tabor University, Gonder, Ethiopia.

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