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September 10, 2025Statistics Optimization & Information Computing

Machine Learning Models for Predicting COVID-19 Mortality Using Epidemiological Features

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Authors

SKSokaina El KhamlichiLTLoubna Taidi

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Overview

Analysis reveals machine learning predicts COVID-19 mortality in hospitalized patients, highlighting crucial factors for care.

Key Points

  • The Random Forest model achieved 89.44% accuracy in predicting COVID-19 mortality, demonstrating its capability in healthcare.
  • Feature selection identified key predictors like Age and Pneumonia, illuminating critical risk factors for fatality.
  • Sampling techniques, including SMOTE and RUS, addressed class imbalance in the COVID-19 dataset, enhancing model performance.
  • This research underscores the importance of using machine learning methods for informed clinical decision-making during pandemics.

Cite This Study

Khamlichi et al. (2025) studied this question.

synapsesocial.com/papers/68c1b81254b1d3bfb60ebe4dhttps://doi.org/10.19139/soic-2310-5070-2159
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Also Consider

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  1. 1Predicting mortality outcomes in individual COVID-19 patients using machine learning algorithms2024 · 1 citations
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  4. 4Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis2024
  5. 5Data mining approach to predicting of death in patients with COVID-192026