Document Type
Article
Publication Date
2026
DOI
10.3390/covid6010017
Publication Title
COVID
Volume
6
Issue
1
Pages
17
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.
Rights
© 2026 by the authors.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.
Data Availability
Article states: "The raw UTMB dataset, the R codes for data wrangling and imputation, and the complete set of analyses reported in this paper are available online [26,27,44]."
Original Publication Citation
Xu, H., Anum, A. T., Pokojovy, M., Madathil, S. C., Wen, Y., Rahman, M. F., Tseng, T.-L., Moen, S., & Walser, E. (2026). Utilizing machine learning techniques for computer-aided COVID-19 screening based on clinical data. COVID, 6(1), Article 17. https://doi.org/10.3390/covid6010017
ORCID
0000-0002-2122-2572 (Pokojovy)
Repository Citation
Xu, Honglun; Anum, Andrews T.; Pokojovy, Michael; Madathil, Sreenath Chalil; Wen, Yuxin; Rahman, Md Fashiar; Tseng, Tzu-Liang (Bill); Moen, Scott; and Walser, Eric, "Utilizing Machine Learning Techniques for Computer-Aided COVID-19 Screening Based on Clinical Data" (2026). Mathematics & Statistics Faculty Publications. 310.
https://digitalcommons.odu.edu/mathstat_fac_pubs/310
Included in
Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons, Theory and Algorithms Commons