The Tohoku journal of experimental medicine
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Tohoku J. Exp. Med. · Jan 2024
Evaluation of Statistical Approaches in Developing a Predictive Model of Severe COVID-19 during Early Phase of Pandemic with Limited Data Resources.
As evidence of risk factors for severe cases of coronavirus disease 2019 (COVID-19) was uncertain in early phases of the pandemic, the development of an efficient predictive model for severe cases to triage high-risk individuals represented an urgent yet challenging issue. It is crucial to select appropriate statistical models when available data and evidence are limited. This study was conducted to assess the accuracy of different statistical models in predicting severe cases using demographic data from patients with COVID-19 prior to the emergence of consequential variants. ⋯ The benefit of performing feature selection with a training dataset before building models was seen in some models, but not in all models. In summary, the naïve Bayes and RF models exhibited ideal predictive performance even with limited available data. The benefit of performing feature selection before building models with limited data resources depended on machine learning methods and parameters.