Enhancing COVID-19 Prognosis Prediction with Machine Learning and LIME Explanation
DOI:
https://doi.org/10.5753/jidm.2025.4678Keywords:
COVID-19, Machine Learning, Computer Aided Prognosis, Mortality Prediction, Explainable AIAbstract
This study evaluates machine learning methods to predict the prognosis of patients in COVID-19 context. This study evaluates machine learning methods for predicting patient prognosis in the COVID-19 context. For the best-performing algorithm, we applied LIME to assess feature contributions to each decision, providing insights to assist experts in understanding the rationale behind the model's predictions. The results indicate that the developed model accurately predicted patient prognosis, achieving an ROC-AUC = 0.8524. The results also point out a higher risk of death among patients over 60 years of age, with comorbidities, and symptoms such as dyspnea and Oxygen saturation <95%, confirming results observed in other regions of the world. The results also indicated a higher percentage of deaths among those with little or no education.
The prediction explanations allowed us to understand how each feature contributes to the decision made by the model, improving its transparency. For instance, in an illustrative case, LIME demonstrated that invasive ventilatory support and an age of 61 years positively contributed to the prediction of mortality, whereas hospitalization and the patient's race (being white) were not significant predictors for this particular patient.
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Figuerêdo, J., Araujo-Calumby, R., and Calumby, R. (2023). Towards effective and reliable data-driven prognostication: An application to covid-19. In Anais do XI Symposium on Knowledge Discovery, Mining and Learning, pages 81–88, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/kdmile.2023.232894.
Figuerêdo, J. et al. (2021). Machine learning for prognosis of patients with covid-19: An early days analysis. In Anais do XVIII ENIAC, pages 59–70, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/eniac.2021.18241.
Grasselli, G. et al. (2020). Baseline Characteristics and Outcomes of 1591 Patients Infected With SARS-CoV-2 Admitted to ICUs of the Lombardy Region, Italy. JAMA, 323(16):1574–1581. DOI: 10.1001/jama.2020.5394.
Islam, M. N. et al. (2020). A survey on the use of AI and ML for fighting the COVID-19 pandemic. CoRR, abs/2008.07449.
Kumar, A. et al. (2020). A review of modern technologies for tackling covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4):569 – 573. DOI: https://doi.org/10.1016/j.dsx.2020.05.008.
Lu, R. et al. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. The Lancet, 395(10224):565–574. DOI: 10.1016/S0140-6736(20)30251-8.
Mattos, J. et al. (2020). Clinical risk factors of icu & fatal covid-19 cases in brazil. In Anais do VIII KDMiLe, pages 33–40, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/kdmile.2020.11956.
Mittelstadt, B. et al. (2019). Explaining explanations in ai. In Proceedings of the Conference on FAT, page 279–288, New York, NY, USA. ACM. DOI: 10.1145/3287560.3287574.
Niquini, R. P. et al. (2020). Description and comparison of demographic characteristics and comorbidities in sari from covid-19, sari from influenza, and the brazilian general population. CSP, 36. DOI: 10.1590/0102-311x00149420.
Onder, G., Rezza, G., and Brusaferro, S. (2020). Case-Fatality Rate and Characteristics of Patients Dying in Relation to COVID-19 in Italy. JAMA, 323(18):1775–1776. DOI: 10.1001/jama.2020.4683.
Pan, D. et al. (2020). A predicting nomogram for mortality in patients with covid-19. Frontiers in Public Health, 8:461. DOI: 10.3389/fpubh.2020.00461.
Pijls, B. G. et al. (2021). Demographic risk factors for covid-19 infection, severity, icu admission and death: a meta-analysis of 59 studies. BMJ Open, 11(1). DOI: 10.1136/bmjopen-2020-044640.
Ribeiro, M. T. et al. (2016). “why should i trust you”: Ex-plaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD, page 1135–1144, New York, USA. ACM. DOI: 10.1145/2939672.2939778.
Richardson, S. et al. (2020). Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area. JAMA, 323(20):2052–2059. DOI: 10.1001/jama.2020.6775.
Rod, J., Oviedo-Trespalacios, O., and Cortes-Ramirez, J. (2020). A brief-review of the risk factors for covid-19 severity. Revista de Saúde Pública, 54:60. DOI: 10.11606/s1518-8787.2020054002481.
Sharma, S., Jackson, P., and Makan, J. (2004). Cardiac troponins. JCP, 57:1025–1026. DOI: 10.1136/jcp.2003.015420.
Soares, F. et al. (2021). Analysis and prediction of childhood pneumonia deaths using machine learning algorithms. In Anais do IX KDMiLe, pages 16–23, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/kdmile.2021.17456.
Souza, F. S. H. et al. (2020). Predicting the disease outcome in covid-19 positive patients through machine learning: a retrospective cohort study with brazilian data. medRxiv. DOI: 10.1101/2020.06.26.20140764.
White, D. B. and Lo, B. (2020). A Framework for Rationing Ventilators and Critical Care Beds During the COVID-19 Pandemic. JAMA, 323(18):1773–1774. DOI: 10.1001/jama.2020.5046.
WHO (2021). Coronavirus disease 2019 Situation Report. [link]. Accessed 01 April 2021. Xie, J. et al. (2020). Association Between Hypoxemia and Mortality in Patients With COVID-19. Mayo Clinic Proceedings, 95(6):1138–1147. DOI: 10.1016/j.mayocp.2020.04.006.
Yan, L. et al. (2020). An interpretable mortality prediction model for COVID-19 patients. Nat. Mach. Intell., 2(5):283–288. DOI: 10.1038/s42256-020-0180-7.
Yu, K.-H. et al. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10):719–731. DOI: 10.1038/s41551-018-0305-z.
Zhou, F. et al. (2020). Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. The Lancet, 395(10229):1054–1062. DOI: 10.1016/S0140-6736(20)30566-3.
