Document Type
Article
Publication Date
1-8-2026
Abstract
The COVID-19 pandemic has highlighted the importance of rapid clinical decision-making to facilitate the efficient usage of healthcare resources. Over the past decade, machine learning (ML) has caused a tectonic shift in healthcare, empowering data-driven prediction and decision-making. Recent research demonstrates how ML was used to respond to the COVID-19 pandemic. This paper puts forth new computer-aided COVID-19 disease screening techniques using six classes of ML algorithms (including penalized logistic regression, random forest, artificial neural networks, and support vector machines) and evaluates their performance when applied to a real-world clinical dataset containing patients’ demographic information and vital indices (such as sex, ethnicity, age, pulse, pulse oximetry, respirations, temperature, BP systolic, BP diastolic, and BMI), as well as ICD-10 codes of existing comorbidities, as attributes to predict the risk of having COVID-19 for given patient(s). Variable importance metrics computed using a random forest model were used to reduce the number of important predictors to thirteen. Using prediction accuracy, sensitivity, specificity, and AUC as performance metrics, the performance of various ML methods was assessed, and the best model was selected. Our proposed model can be used in clinical settings as a rapid and accessible COVID-19 screening technique.
Recommended Citation
Xu, H.; Anum, A.T.; Pokojovy, M.; Madathil, S.C.; Wen, Y.; Rahman, M.F.; Tseng, T.-L.; Moen, S.; Walser, E. Utilizing Machine Learning Techniques for Computer-Aided COVID-19 Screening Based on Clinical Data. COVID 2026, 6, 17. https://doi.org/10.3390/covid6010017
Copyright
The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
This article was originally published in COVID, volume 6, issue 1, in 2026. https://doi.org/10.3390/covid6010017
This scholarship is part of the Chapman University COVID-19 Archives.