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Purpose <p>Global healthcare systems faced substantial challenges during the COVID-19 pandemic, which emphasized the requirement for precise methods to predict patient outcomes/mortality. The present work uses patients’ biomarker data to evaluate the potential of 14 different machine learning (ML) models in predicting COVID-19 patient outcomes, i.e., survival vs. non-survival.</p> Methods <p>ML models were initially supplied with 26 clinical biomarkers of patients available in public repositories. Models’ performance parameters such as accuracy, precision, recall, specificity, AUPRC, and AUC-ROC are evaluated, and then models are accordingly ranked to identify the five best-performing models. SHAP plots identified 15 common and most influential biomarkers (including two demographic features i.e., age and gender) regulating the patient outcome. Lower-ranked models and less-influential biomarkers were ablated. Five best-performing models, LightGBM, Random Forest, CatBoost, XGBoost, and Gradient Boost, are trained and tested using 15 most influential biomarkers on two patient cohorts separately.</p> Results <p>Bland–Altman analysis affirmed that these models closely predict the clinical outcomes, i.e., survival and non-survival in patient groups of different ages and genders. In addition, partial dependence plots and paired <i>t</i>-tests displayed that ML models successfully captured biomarkers’ clinical role in deciding patient mortality. LightGBM exhibited better performance than the other four ML models. Thus, based on day-wise biomarker data, identified ML models can be potentially utilized in predicting COVID-19 patient outcomes.</p> Conclusion <p>Overall, the outcomes can be beneficial in monitoring the disease progression, rapid resource allocation in high-mortality areas, and clinical management of critical patients.</p>

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Computational and statistical insights for leveraging biomarker-driven machine learning models in predicting COVID-19 patient outcomes

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Jitendra Mehta,
  • Sayani Das,
  • Vibhav Prakash Singh,
  • Ramesh Pandey,
  • Ravi Prakash Tewari,
  • Abhishek Kumar Tiwari

摘要

Purpose

Global healthcare systems faced substantial challenges during the COVID-19 pandemic, which emphasized the requirement for precise methods to predict patient outcomes/mortality. The present work uses patients’ biomarker data to evaluate the potential of 14 different machine learning (ML) models in predicting COVID-19 patient outcomes, i.e., survival vs. non-survival.

Methods

ML models were initially supplied with 26 clinical biomarkers of patients available in public repositories. Models’ performance parameters such as accuracy, precision, recall, specificity, AUPRC, and AUC-ROC are evaluated, and then models are accordingly ranked to identify the five best-performing models. SHAP plots identified 15 common and most influential biomarkers (including two demographic features i.e., age and gender) regulating the patient outcome. Lower-ranked models and less-influential biomarkers were ablated. Five best-performing models, LightGBM, Random Forest, CatBoost, XGBoost, and Gradient Boost, are trained and tested using 15 most influential biomarkers on two patient cohorts separately.

Results

Bland–Altman analysis affirmed that these models closely predict the clinical outcomes, i.e., survival and non-survival in patient groups of different ages and genders. In addition, partial dependence plots and paired t-tests displayed that ML models successfully captured biomarkers’ clinical role in deciding patient mortality. LightGBM exhibited better performance than the other four ML models. Thus, based on day-wise biomarker data, identified ML models can be potentially utilized in predicting COVID-19 patient outcomes.

Conclusion

Overall, the outcomes can be beneficial in monitoring the disease progression, rapid resource allocation in high-mortality areas, and clinical management of critical patients.