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English Information

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Authors
# Name
1 Mateus Lopes(lopes.mateus@sou.ufac.br)
2 Manoel de Lima Júnior(manoel.limeira@ufac.br)
3 Rafael Braga(alves.rafael@sou.ufac.br)

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Reference
# Reference
1 Adib, Q. A. R., Tasmi, S. T., Bhuiyan, S. I., Raihan, M. S., and Shams, A. B. (2021). Prediction model for mortality analysis of pregnant women affected with covid-19. In 2021 24th International Conference on Computer and Information Technology (IC-CIT), pages 1–6. IEEE.
2 Agência de Notı́cias do Acre (2021). Acre inicia programa de imunização contra a COVID-19 e apresenta seus primeiros vacinados. Acessado em: 17 jan. 2026.
3 Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY, USA. Association for Computing Machinery.
4 COFEN (2020). Ministério da saúde orienta conduta para casos suspeitos de reinfecção por COVID-19.
5 Da Silva, E. L. and Menezes, E. M. (2005). Metodologia da pesquisa e elaboração de dissertação. UFSC, Florianópolis, 4a. edição, 123(4):138.
6 Han, R., Liu, Z., Philip Chen, C., Xu, L., and Peng, G. (2020). Mortality prediction for covid-19 patients via broad learning system. In 2020 7th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS), pages 837–842. IEEE.
7 Júnior, M. L. d. L., Soares, D. M., Plastino, A., and Murta, L. (2018). Automatic assignment of integrators to pull requests: The importance of selecting appropriate attributes. J. Syst. Softw., 144(C):181–196.
8 Kaufman, S., Rosset, S., Perlich, C., and Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Trans. Knowl. Discov. Data, 6(4).
9 Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2018). Learning under concept drift: A review. IEEE Transactions on Knowledge and Data Engineering, page 1–1.
10 Sahiner, B., Chen, W., Samala, R. K., and Petrick, N. (2023). Data drift in medical machine learning: implications and potential remedies. The British Journal of Radiology, 96(1150):20220878.
11 Silva, R. and Silva Neto, D. R. d. (2023). Inteligência artificial e previsão de óbito por Covid-19 no Brasil: uma análise comparativa entre os algoritmos Logistic Regression, Decision Tree e Random Forest. Saúde em Debate, 46:118–129. Publisher: Centro Brasileiro de Estudos de Saúde.
12 Solanki, V., Solanki, U., Baliyan, A., Kukreja, V., Lamba, V., and Sahoo, B. K. (2021). Importance of artificial intelligence and machine learning in fighting with covid-19 epidemic. In 2021 5th International Conference on Information Systems and Computer Networks (ISCON), pages 1–8. IEEE.
13 Walia, H. and Jeevaraj, S. (2021). Early mortality risk prediction in covid-19 patients using an ensemble of machine learning models. In 2021 International Conference on Computational Performance Evaluation (ComPE), pages 965–970. IEEE.