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Authors
# Name
1 izadora ganem(izadoraganem@gmail.com)
2 Guilherme Dal Bianco(guilherme.dalbianco@uffs.edu.br)
3 José Carlos Filho(serufo@ufmg.br)
4 Luciano de Lima(luciano@dcc.ufmg.br)
5 Leonardo Rocha(lcrocha@ufsj.edu.br)
6 Marcos André Gonçalves(mgoncalv@dcc.ufmg.br)

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Reference
# Reference
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2 Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.
3 Chen, Z., Tan, S., Chajewska, U., Rudin, C., and Caruana, R. (2023). Missing values and imputation in healthcare data: Can interpretable machine learning help? In Proceedings of the Conference on Health, Inference, and Learning (CHIL), volume 209, pages 88–108. PMLR
4 Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., and Tabona, O. (2021). A survey on missing data in machine learning. Journal of Big data, 8:1–37.
5 Jarrett, D., Cebere, B., Liu, T., Curth, A., and van der Schaar, M. (2022). Hyperimpute: Generalized iterative imputation with automatic model selection.
6 Lana, F. C. B., Marinho, C. C., de Paiva, B. B. M., Valle, L. R., do Nascimento, G. F., da Rocha, L. C. D., Carneiro, M., Batista, J. d. L., Anschau, F., Paraiso, P. G., Bartolazzi, F., Cimini, C. C. R., Schwarzbold, A. V., Rios, D. R. A., Gonc¸alves, M. A., and Marcolino, M. S. (2025). Unraveling relevant cross-waves pattern drifts in patient hospital risk factors among hospitalized covid-19 patients using explainable machine learning methods. BMC Infectious Diseases, 25(1):537.
7 Little, R. J. and Rubin, D. B. (2019). Statistical analysis with missing data. John Wiley & Sons.
8 Liu, M., Li, S., Yuan, H., Ong, M. E. H., Ning, Y., Xie, F., Saffari, S. E., Shang, Y., Volovici, V., Chakraborty, B., et al. (2023). Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial intelligence in medicine, 142:102587.
9 Marcolino, M. S., Ziegelmann, P. K., Souza-Silva, M. V. R., Nascimento, I. J. B., Oliveira, L. M., and et al. (2021). Clinical characteristics and outcomes of patients hospitalized with covid-19 in brazil: Results from the brazilian covid-19 registry. International Journal of Infectious Diseases, 107:300–310.
10 Paiva, B. B. M. et al. (2023). Potential and limitations of machine metalearning (ensemble) methods for predicting covid-19 mortality in a large in-hospital brazilian dataset. Scientific Reports, 13(1):3463.
11 Shadbahr, T., Roberts, M., Stanczuk, J., Gilbey, J., Teare, P., Dittmer, S., Thorpe, M., Torné, R. V., Sala, E., Li´o, P., Patel, M., Preller, J., Rudd, J. H. F., Mirtti, T., Rannikko, A. S., Aston, J. A. D., Tang, J., and Schönlieb, CB. (2023). The impact of imputation quality on machine learning classifiers for datasets with missing values. Communications Medicine, 3(1):139.
12 Yoon, J., Jordon, J., and van der Schaar, M. (2018). Gain: Missing data imputation using generative adversarial nets. In Proceedings of the 35th International Conference on Machine Learning (ICML), pages 5689–5698. PMLR.