ArticleBMC public health2025
Prediction models based on machine learning algorithms for COVID-19 severity risk.
Article in BMC public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
- Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.PloS one · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe World Health Organization has highlighted the risk of Disease X, urging pandemic preparedness. Coronavirus disease 2019 (COVID-19) could be the first Disease X; therefore, understanding the epidemiological experiences of COVID-19 is crucial while preparing for future similar diseases.
methodsPrediction models for COVID-19 severity risk in hospitalized patients were constructed based on four machine learning algorithms, namely, logistic regression, Cox regression, support vector machine (SVM), and random forest. These models were evaluated for prediction accuracy, area under the curve (AUC), sensitivity, and specificity as well as were interpreted using SHapley Additive exPlanation.
resultsData were collected from 1,485 hospitalized patients across 6 centers, comprising 1,184 patients with severe or critical COVID-19 and 301 patients with nonsevere COVID-19. Among the four models, the SVM model achieved the highest prediction accuracy of 98.45%, with an AUC of 0.994, a sensitivity of 0.989, and a specificity of 0.969. Moreover, oxygenation index (OI), confusion, respiratory rate, and age were found to be predictors of COVID-19 severity risk.
conclusionsSVM could accurately predict COVID-19 severity risk; thus, it can be prioritized as a prediction model. OI is the most critical predictor of COVID-19 severity risk and can serve as the primary and independent evaluation indicator.
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