Machine Learning Models for Predicting COVID-19 Mortality Using Epidemiological Features
Keywords:
COVID-19, epidemiology, machine learning, threat Management, imbalanced dataset, RUS, SMOTE, ADASYN
Abstract
Identifying COVID-19 patients at high risk of fatality is critically important for healthcare professionals, as it supports informed decision-making and enhances the capacity to manage emerging crises within medical systems. Nevertheless, COVID-19 datasets are frequently highly imbalanced, with substantially fewer fatality cases presenting a challenge to the development of effective machine learning algorithms. This study aims to develop a high-performing machine learning approach to predict COVID-19 mortality using a Mexican epidemiological dataset. To tackle the class imbalance issue, numerous sampling techniques are applied, including SMOTE, SMOTE-ENN, ADASYN, SMOTE-Tomek, and Random Under-Sampling (RUS). Predictive models are created using several machine learning algorithms: Logistic Regression, Decision Tree, Gaussian Naïve Bayes, K-Nearest Neighbors, and Random Forest. Besides, we performed feature selection analysis using Shap technique to determine the main relevant attributes for predicting COVID-19 mortality. The results illustrate that Random Forest model, trained on balanced data with SMOTE-ENN technique yielded the best performance, with 89.44% accuracy, 87.88% Recall, and 88.74% ROC AUC score. Furthermore, feature selection analysis shows that Type of Patient, Age, Pneumonia, Intubation, having contact with COVID-19 infected patients are the key important attributes for predicting COVID-19 risk of fatality among hospitalized individuals.References
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8. N. Salari, H. Khazaie, A. Hosseinian-Far, B. Khaledi-Paveh, M. Kazeminia, M. Mohammadi, S. Shohaimi, A. Daneshkhah, and S. Eskandari, The prevalence of stress, anxiety and depression within front-line healthcare workers caring for COVID-19 patients: a systematic review and meta-regression, Human Resources for Health, vol. 18, pp. 1–14, 2020.
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10. C. Iwendi, C. G. Y. Huescas, C. Chakraborty, and S. Mohan, COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients, Journal of Experimental & Theoretical Artificial Intelligence, vol. 36, no. 3, pp. 315–335, 2024.
11. S.Wollenstein-Betech, C. G. Cassandras, and I. Ch. Paschalidis, Personalized predictive models for symptomatic COVID-19 patients using basic preconditions: hospitalizations, mortality, and the need for an ICU or ventilator, International Journal of Medical Informatics, vol. 142, pp. 104258, 2020.
12. S. Bolourani, M. Brenner, P. Wang, T. McGinn, J. Hirsch, D. Barnaby, and T. Zanos, Development and Validation of a Machine learning prediction model of respiratory failure within 48 hours of patient admission for COVID-19, Journal of Medical Internet Research, 2021.
13. H. Mohammedqasim and O. Ata, Real-time data of COVID-19 detection with IoT sensor tracking using artificial neural network, Computers and Electrical Engineering, vol. 100, pp. 107971, 2022.
14. J. Wu, J. Shen, M. Xu, and M. Shao, A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count, Computer Methods and Programs in Biomedicine, vol. 211, pp. 106444, 2021.
15. M. AlJame, I. Ahmad, A. Imtiaz, and A. Mohammed, Ensemble learning model for diagnosing COVID-19 from routine blood tests, Informatics in Medicine Unlocked, vol. 21, pp. 100449, 2020.
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18. K. Fujiwara, Y. Huang, K. Hori, K. Nishioji, M. Kobayashi, M. Kamaguchi, and M. Kano, Over-and under-sampling approach for extremely imbalanced and small minority data problem in health record analysis, Frontiers in Public Health, vol. 8, pp. 178, 2020.
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20. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, SMOTE: Synthetic minority over-sampling technique, Journal of Artificial Intelligence Research, vol. 16, pp. 321–357, 2002.
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30. M. Saripuddin, A. Suliman, S. S. Sameon, and B. N. Jorgensen, Random undersampling on imbalance time series data for anomaly detection, in Proceedings of the 2021 4th International Conference on Machine Learning and Machine Intelligence, pp. 151–156, 2021.
31. A. Farshidvard, F. Hooshmand, and S. A. MirHassani, A novel two-phase clustering-based under-sampling method for imbalanced classification problems, Expert Systems with Applications, vol. 213, pp. 119003, 2023.
32. J. M. Johnson and T. M. Khoshgoftaar, Survey on deep learning with class imbalance, Journal of Big Data, vol. 6, no. 1, pp. 1–54, 2019.
33. A. El Hariri, M. Mouiti, O. Habibi, and M. Lazaar, Improving Deep Learning Performance Using Sampling Techniques for IoT Imbalanced Data, Procedia Computer Science, vol. 224, pp. 180–187, 2023.
Published
2025-05-28
How to Cite
EL KHAMLICHI, S., & Loubna Taidi. (2025). Machine Learning Models for Predicting COVID-19 Mortality Using Epidemiological Features. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2159
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Section
Research Articles
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