Critical Determinants of COVID-19 Severity and Predictive Modeling for Healthcare Optimization
Article Sidebar
Main Article Content
Abstract
Abstract
The COVID-19 pandemic placed unprecedented strain on global healthcare systems, highlighting the need to identify critical determinants of disease severity and develop predictive models for resource optimization. This study aimed to identify the most significant factors influencing COVID-19 severity, analyze comorbidity patterns, and develop machine learning models for predicting severe outcomes. Using a dataset of 1,000 COVID-19 patients, demographic, clinical, and medical history data were analyzed. Comorbidities such as COPD (96.3%), chronic renal disease (92.6%), cardiovascular issues (93.9%), and diabetes (69.9%) were found to be highly prevalent among severe cases. Over half of the patients required ICU admission (51.1%) or ventilator support (54.5%), indicating the critical impact of severe COVID-19 symptoms on healthcare systems. Four machine learning models decision tree, logistic regression, random forest, and AdaBoost were evaluated for predictive accuracy using a 20-80 ratio and 10-fold cross-validation. In the 20-80 ratio, AdaBoost and logistic regression emerged as the most effective models, achieving 77.00% accuracy, with AdaBoost excelling in precision at 79.84% and specificity at 91.75%, and Logistic Regression providing the highest sensitivity at 67.96% for balanced predictions. The average results across all folds were as follows: Decision Tree accuracy was 65.80%, Random Forest accuracy was 72.40%, Logistic Regression accuracy was 75.40%, and AdaBoost accuracy was 75.50%. These findings underscore the importance of comorbidities in determining COVID-19 severity and demonstrate the utility of predictive modeling in optimizing healthcare resources. The study concludes that tailored interventions for high-risk patients and machine learning-driven resource allocation strategies can enhance healthcare efficiency during pandemics.
Downloads
Article Details
Copyright (c) 2025 Uallah H, et al.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing and protecting the author rights is the central aim and core of the publishing business. Peertechz dedicates itself in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. Peertechz licensing terms are formulated to facilitate reuse of the manuscripts published in journals to take maximum advantage of Open Access publication and for the purpose of disseminating knowledge.
We support 'libre' open access, which defines Open Access in true terms as free of charge online access along with usage rights. The usage rights are granted through the use of specific Creative Commons license.
Peertechz accomplice with- [CC BY 4.0]
Explanation
'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.
Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.
With this license, the authors are allowed that after publishing with Peertechz, they can share their research by posting a free draft copy of their article to any repository or website.
'CC BY' license observance:
|
License Name |
Permission to read and download |
Permission to display in a repository |
Permission to translate |
Commercial uses of manuscript |
|
CC BY 4.0 |
Yes |
Yes |
Yes |
Yes |
The authors please note that Creative Commons license is focused on making creative works available for discovery and reuse. Creative Commons licenses provide an alternative to standard copyrights, allowing authors to specify ways that their works can be used without having to grant permission for each individual request. Others who want to reserve all of their rights under copyright law should not use CC licenses.
Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Bernardini S. The COVID-19 pandemic. Crit Rev Clin Lab Sci. 2020;57(6):365-388. Available from: https://doi.org/10.1080/10408363.2020.1783198
Suryasa IW, Rodríguez-Gámez M, Koldoris T. The COVID-19 pandemic. Int J Health Sci. 2021;5(2):6-9. Available from: https://doi.org/10.53730/ijhs.v5n2.2937
Daniel SJ. Education and the COVID-19 pandemic. Prospects. 2020;49(1):91-96. Available from: https://link.springer.com/article/10.1007/s11125-020-09464-3
Baloch S, Baloch MA, Zheng T, Pei X. The Coronavirus Disease 2019 (COVID-19) Pandemic. Tohoku J Exp Med. 2020;250(4):271-278. Available from: https://doi.org/10.1620/tjem.250.271
Omer SB, Malani P, Del Rio C. The COVID-19 pandemic in the US: a clinical update. JAMA. 2020;323(18):1767-1768. Available from: https://jamanetwork.com/journals/jama/fullarticle/2764366
Nour TY, Altintaş KH. Effect of the COVID-19 pandemic on obesity and its risk factors: A systematic review. BMC Public Health. 2023;23(1):1018. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-023-15833-2
Jordan RE, Adab P, Cheng KK. Covid-19: risk factors for severe disease and death. BMJ. 2020;368:m1198. Available from: https://doi.org/10.1136/bmj.m1198
Chen Q, Li W, Xiong J, Zheng X. Prevalence and risk factors associated with postpartum depression during the COVID-19 pandemic: a literature review and meta-analysis. Int J Environ Res Public Health. 2022;19(4):2219. Available from: https://doi.org/10.3390/ijerph19042219
Woo HG, Park S, Yon H, Lee SW, Koyanagi A, Jacob L, Smith L, et al. National trends in sadness, suicidality, and COVID-19 pandemic–related risk factors among South Korean adolescents from 2005 to 2021. JAMA Netw Open. 2023;6(5):e2314838. Available from: https://doi.org/10.1001/jamanetworkopen.2023.14838
Cena H, Fiechtner L, Vincenti A, Magenes VC, De Giuseppe R, Manuelli M, et al. COVID-19 pandemic as risk factors for excessive weight gain in pediatrics: the role of changes in nutrition behavior. A narrative review. Nutrients. 2021;13(12):4255. Available from: https://doi.org/10.3390/nu13124255
van Loon AW, Creemers HE, Vogelaar S, Miers AC, Saab N, Westenberg PM, et al. Prepandemic risk factors of COVID‐19‐related concerns in adolescents during the COVID‐19 pandemic. J Res Adolesc. 2021;31(3):531-545. Available from: https://doi.org/10.1111/jora.12651
Wolff D, Nee S, Hickey NS, Marschollek M. Risk factors for Covid-19 severity and fatality: a structured literature review. Infection. 2021;49:15-28. Available from: https://doi.org/10.1007/s15010-020-01509-1
Zhang J, Wang X, Jia X, Li J, Hu K, Chen G, et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect. 2020;26(6):767-772. Available from: https://doi.org/10.1016/j.cmi.2020.04.012
Heidari A, Jafari Navimipour N, Unal M, Toumaj S. Machine learning applications for COVID-19 outbreak management. Neural Comput Appl. 2022;34(18):15313-15348. Available from: https://link.springer.com/article/10.1007/s00521-022-07424-w
Syeda HB, Syed M, Sexton KW, Syed S, Begum S, Syed F, et al. Role of machine learning techniques to tackle the COVID-19 crisis: systematic review. JMIR Med Inform. 2021;9(1):e23811. Available from: https://doi.org/10.2196/23811
Kwekha-Rashid AS, Abduljabbar HN, Alhayani B. Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl Nanosci. 2023;13(3):2013-2025. Available from: https://link.springer.com/article/10.1007/s13204-021-01868-7
Muhammad LJ, Algehyne EA, Usman SS, Ahmad A, Chakraborty C, Mohammed IA. Supervised Machine Learning Models for Prediction of COVID-19 Infection using Epidemiology Dataset. SN Comput Sci. 2021;2(1):11. Available from: https://doi.org/10.1007/s42979-020-00394-7
Alaufi R, Kalkatawi M, Abukhodair F. Challenges of deep learning diagnosis for COVID-19 from chest imaging. Multimed Tools Appl. 2024;83(5):14337-14361. Available from: https://link.springer.com/article/10.1007/s11042-023-16017-1
Alakus TB, Turkoglu I. Comparison of deep learning approaches to predict COVID-19 infection. Chaos Solitons Fractals. 2020;140:110120. Available from: https://doi.org/10.1016/j.chaos.2020.110120
John CC, Ponnusamy V, Krishnan Chandrasekaran S, R N. A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis. IEEE Rev Biomed Eng. 2022;15:325-340. Available from: https://doi.org/10.1109/rbme.2021.3069213
Ahmad A, Garhwal S, Ray SK, Kumar G, Malebary SJ, Barukab OM. The number of confirmed cases of COVID-19 by using machine learning: Methods and challenges. Arch Comput Methods Eng. 2021;28:2645-2653. Available from: https://doi.org/10.1007/s11831-020-09472-8
Liu Q, Nair R, Huang R, Zhu H, Anderson A, Belen O, et al. Using machine learning to determine a suitable patient population for anakinra for the treatment of COVID‐19 under the emergency use authorization. Clin Pharmacol Ther. 2024;115(4):890-895. Available from: https://doi.org/10.1002/cpt.3191
Patil D, Rane N, Desai P, Rane J. Machine learning and deep learning: Methods, techniques, applications, challenges, and future research opportunities. Trustworthy Artificial Intelligence in Industry and Society. 2024:28-81. Available from: http://dx.doi.org/10.70593/978-81-981367-4-9_2
Modhugu VR, Ponnusamy S. Comparative analysis of machine learning algorithms for liver disease prediction: SVM, logistic regression, and decision tree. Asian J Res Comput Sci. 2024;17(6):188-201. Available from: https://journalajrcos.com/index.php/AJRCOS/article/view/467
Zhou X, Zhang J, Deng XM, Fu FM, Wang JM, Zhang ZY, et al. Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections. Sci Rep. 2024;14(1):22673. Available from: https://www.nature.com/articles/s41598-024-74057-5
Liu P, Xing Z, Peng X, Zhang M, Shu C, Wang C, et al. Machine learning versus multivariate logistic regression for predicting severe COVID‐19 in hospitalized children with Omicron variant infection. J Med Virol. 2024;96(2):e29447. Available from: https://doi.org/10.1002/jmv.29447
Suliman M, Malik F, Qasim Khan M, Irfan Ullah, Abd Ur Rub. Integrating data augmentation with AdaBoost for effective COVID-19 pneumonia classification. J Comput Biomed Informatics. 2024;7(01):590-605. Available from: https://jcbi.org/index.php/Main/article/view/512