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Severity Detection for the Coronavirus Disease 2019 (COVID-19) Patients Using a Machine Learning Model Based on the
Haochen Yao1, Nan Zhang2, Ruochi Zhang3
1Department of Pathogenobiology, The Key Laboratory of Zoonosis, Chinese Ministry of Education, College of Basic Medical Science, Jilin University, Changchun, China.
Insights
Machine learning models can predict severe COVID-19 outcomes. A Support Vector Machine (SVM) model using 28 biomarkers achieved 81.48% accuracy in detecting COVID-19 severity, aiding risk assessment.
Area of Science:
- Infectious Diseases
- Computational Biology
- Biomarkers
Background:
- The COVID-19 pandemic poses significant global health challenges, causing severe pneumonia and potential organ failure.
- Understanding factors predicting disease severity is crucial for patient management and resource allocation.
Purpose of the Study:
- To develop and validate a machine learning model for detecting COVID-19 severity.
- To identify key biomarkers associated with severe COVID-19 outcomes.
Main Methods:
- Utilized machine learning algorithms, specifically Support Vector Machine (SVM).
- Identified 32 features significantly associated with COVID-19 severity.
- Screened features for redundancy, finalizing a model with 28 biomarkers.
Main Results:
- The final SVM model achieved an overall accuracy of 0.8148 in predicting COVID-19 severity.
- 28 distinct features were identified as significant predictors of severe disease.
Conclusions:
- The developed SVM model shows promise for estimating the risk of severe COVID-19.
- The identified 28 biomarkers warrant further investigation into their underlying mechanisms in COVID-19 pathogenesis.
Abstract:
The recent outbreak of the coronavirus disease-2019 (COVID-19) caused serious challenges to the human society in China and across the world. COVID-19 induced pneumonia in human hosts and carried a highly inter-person contagiousness. The COVID-19 patients may carry severe symptoms, and some of them may even die of major organ failures. This study utilized the machine learning algorithms to build the COVID-19 severeness detection model. Support vector machine (SVM) demonstrated a promising detection accuracy after 32 features were detected to be significantly associated with the COVID-19 severeness. These 32 features were further screened for inter-feature redundancies. The final SVM model was trained using 28 features and achieved the overall accuracy 0.8148. This work may facilitate the risk estimation of whether the COVID-19 patients would develop the severe symptoms. The 28 COVID-19 severeness associated biomarkers may also be investigated for their underlining mechanisms how they were involved in the COVID-19 infections.
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