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English Information

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Authors
# Name
1 Marco Sousa(mtulio0906@gmail.com)
2 Luis Zarate(zarate@pucminas.br)

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Reference
# Reference
1 Fernandes, G. and Costa, M. (2021). Machine learning techniques for early detection of chronic kidney disease in brazil. Artificial Intelligence in Medicine, 117(4):153–167.
2 Loyola-González, O. (2019). Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access, 7:154096–154113.
3 Methodology, B. M. R. (2023). Comparison of the effects of imputation methods for missing data in predictive modelling. In Example, C. and Sample, B., editors, BMC Medical Research Methodology, pages 15–20. BioMed Central.
4 MS-BRASIL (2024). Ministério de saúde, brasil. Ministério de Saúde, Brasil, gv.br.
5 Oliveira, A. and Santos, M. (2023). Application of machine learning algorithms in chronic kidney disease prediction: A brazilian study. Journal of Medical Systems, 47(2):289–305.
6 OMS (2022). The global impact of chronic kidney disease. In Division, W. H. S., editor, Global Health Statistics 2023, pages 45–60. World Health Organization.
7 Pereira, L. and Silva, R. (2022). Utilizing machine learning for the diagnosis of chronic kidney disease in brazilian patients. Health Informatics Journal, 28(1):112–125.
8 SBN (2023). Impacto da doença renal crônica no brasil. In Silva, J., editor, Relatório Anual da Sociedade Brasileira de Nefrologia, pages 23–45. Sociedade Brasileira de Nefrologia.
9 Souza, T. and Lima, F. (2022). Predictive models for chronic kidney disease: A study in brazilian population using machine learning. BMC Medical Informatics and Decision Making, 22(3):210–223.