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Individual Factors Associated With COVID-19 Infection: A Machine Learning Study
Tania Ramírez-Del Real1,2, Mireya Martínez-García3, Manlio F Márquez3
1Cátedras Conacyt, National Council on Science and Technology, Mexico City, Mexico.
Frontiers in Public Health
|July 18, 2022
Summary
Machine learning models identified key risk factors for COVID-19 infection, aiding in prevention. The random forest model achieved 90.41% accuracy in predicting SARS-CoV-2 contraction.
Area of Science:
- Computational biology
- Epidemiology
- Health informatics
Background:
- The rapid spread of COVID-19 necessitated advanced diagnostic and prognostic tools.
- Effective management requires identifying factors influencing infection risk.
Purpose of the Study:
- To identify potential factors associated with COVID-19 infection using machine learning.
- To develop a predictive model for SARS-CoV-2 contraction.
Main Methods:
- Feature selection: random forest, chi-squared, XGBoost, rPart.
- Resampling techniques: ROSE, SMOTE for class imbalance.
- Model training: SVM, C4.5, random forest, rPart, deep neural networks.
Main Results:
- XGBoost identified the most significant features linked to COVID-19.
- Random forest model achieved 90.41% balanced accuracy with SMOTE.
- Identified controllable risk factors for SARS-CoV-2 infection.
Conclusions:
- Machine learning models can effectively predict COVID-19 risk.
- Identifying controllable factors aids in preventing SARS-CoV-2 transmission.
- This approach supports health systems in managing the pandemic.
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