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Volume 20, Issue 4
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

Accurately predicting survival in hospitalized COVID-19 patients is crucial but challenging due to multiple risk factors. This study addresses the limitations of existing research by proposing a comprehensive machine-learning framework to identify key mortality risk factors and develop a robust predictive model.

Objective

This study proposes an analytical framework that leverages various machine learning techniques to predict the survival of hospitalized COVID-19 patients accurately. The framework comprehensively evaluates multiple clinical indicators and their associations with mortality risk.

Methods

Patient data, including gender, age, health condition, and smoking habits, was divided into discharged (=507) and deceased (=300) categories. Each patient was characterized by 92 clinical features. The framework incorporated seven feature ranking algorithms (LASSO, LightGBM, MCFS, mRMR, RF, CATBoost, and XGBoost), the IFS method, and four classification algorithms (DT, KNN, RF, and SVM).

Results

Age, diabetes, dyspnea, chronic kidney failure, and high blood pressure were identified as the most important risk factors. The best model achieved an F1-score of 0.857 using KNN with 34 selected features.

Conclusion

Our findings provide a comprehensive analysis of COVID-19 mortality risk factors and develops a robust predictive model. The findings highlight the increased risk in patients with comorbidities, consistent with existing literature. The proposed framework can aid in developing personalized treatment plans and allocating healthcare resources effectively.

© 2025 Bentham Science Publishers
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2025-04-15

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/content/journals/cbio/10.2174/0115748936306893240720192301
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Identifying Key Clinical Indicators Associated with the Risk of Death in Hospitalized COVID-19 Patients
Curr Bioinform 20, 359 (2025); https://doi.org/10.2174/0115748936306893240720192301
/content/journals/cbio/10.2174/0115748936306893240720192301
/content/journals/cbio/10.2174/0115748936306893240720192301
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Supplementary Table : Feature vector of samples from the original dataset; Supplementary Table : Feature ranking results obtained using LASSO, LightGBM, MCFS, mRMR, RF, CATBoost, and XGBoost; Supplementary Table : Performance of IFS with different classification algorithms; Supplementary Table . Intersection of essential feature subsets extracted from LASSO, LightGBM, MCFS, RF, CATBoost, and XGBoost feature lists. The features occurring in 7, 6, 5, 4, 3, 2, and 1 subsets are presented in Supplementary Table . Classification rules generated by the best DT model.

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  • Article Type:     Research Article
Keyword(s): clinical indicator; COVID-19; machine learning; mortality risk; predictive model; risk of death

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