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Establishment of prediction models for COVID-19 patients in different age groups based on Random Forest algorithm
1From the Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, 1163 Xinmin Street, Changchun 130021, China.
QJM : Monthly Journal of the Association of Physicians
|October 20, 2021
Summary
This study developed two random forest models to predict COVID-19 mortality risk in younger and elderly patients. These models offer a simple tool for early mortality prediction in coronavirus disease 2019.
Area of Science:
- Medical research
- Infectious diseases
- Epidemiology
Background:
- Coronavirus disease 2019 (COVID-19) is a global pandemic.
- Age is a significant independent factor influencing COVID-19 mortality.
- There is a need for predictive models to stratify patients by mortality risk.
Purpose of the Study:
- To compare laboratory parameters between younger (≤70) and elderly (>70) COVID-19 patients.
- To develop age-stratified death prediction models for COVID-19 patients.
- To identify optimal diagnostic predictors for clinical prognoses.
Main Methods:
- Retrospective, single-center observational study of 437 hospitalized COVID-19 patients.
- Recursive feature elimination (RFE) for optimal variable selection.
- Two random forest (RF) algorithms built to predict patient prognoses.
Main Results:
- Significant differences in laboratory indicators were observed between younger and elderly groups.
- 11 key variables were identified using RFE for model development.
- RF models achieved AUCs of 0.874 for younger and 0.842 for elderly patients.
Conclusions:
- Two distinct prediction models for COVID-19 mortality were developed using the random forest algorithm.
- These models are based on age stratification (younger vs. elderly).
- The models provide a straightforward tool for early prediction of COVID-19 mortality risk.
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