SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Samuel Alves(samuelnorbertoalves@gmail.com)
2 Celso França(junnior.el@gmail.com)
3 Regina Bernal(reginatomiebernal@gmail.com)
4 Crizian Gomes(criziansaar@gmail.com)
5 Oluwatoyin Omole(omoleoluwatoyin18@gmail.com)
6 Deborah Malta(dcmalta@uol.com.br)
7 Marcos Gonçalves(mgoncalv@dcc.ufmg.br)
8 Jussara Almeida(jussara@dcc.ufmg.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Alnowaiser, K. (2024). Improving healthcare prediction of diabetic patients using knn imputed features and tri-ensemble model. IEEE Access, 12:16783–16793.
2 ANS (2021). Promoção da sáude e prevenção de doenças - PROMOPREV - https://www.gov.br/ans/pt-br/assuntos/operadoras/compromissos-e-interacoes-com- a-ans-1/programas-ans-1/promoprev. Atualizado em 06/06/2025.
3 Banday, M. Z., Sameer, A. S., and Nissar, S. (2020). Pathophysiology of diabetes: An overview. Avicenna journal of medicine, 10(04):174–188.
4 Cunha, W. et al. (2023). An effective, efficient, and scalable confidence-based instance selection framework for transformer-based text classification. In SIGIR, page 665–674.
5 Cunha, W., Moreo Fernández, A., Esuli, A., Sebastiani, F., Rocha, L., and Gonçalves, M. A. (2025). A noise-oriented and redundancy-aware instance selection framework. ACM Trans. Inf. Syst., 43(2).
6 da Cunha Paula, D. J. (2014). Análise de custo e efetividade do tratamento de diabéticos adultos atendidos no centro hiperdia de juiz de fora, minas gerais. Dissertação de mestrado, Universidade Federal de Juiz de Fora, Juiz de Fora, MG, Brasil. Aprovado em 17 de fevereiro de 2014.
7 Dinh, A., Miertschin, S., Young, A., and Mohanty, S. D. (2019). A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Medical Informatics and Decision Making, 19(1):211.
8 Ferreira, T., França, C., A. Gonçalves, M., Pagano, A., et al. (2021). Evaluating recognizing question entailment methods for a Portuguese community question-answering system about diabetes mellitus. In Proc. Int’l Conf. on Recent Advances in Natural Language Processing.
9 França, C., Lima, R. C., Andrade, C., Cunha, W., de Melo, P. O. V., Ribeiro-Neto, B., Rocha, L., Santos, R. L., Pagano, A. S., and Gonçalves, M. A. (2024). On representation learning-based methods for effective, efficient, and scalable code retrieval. Neurocomputing, 600:128172.
10 Glechner, A., Keuchel, L., Affengruber, L., Titscher, V., Sommer, I., Matyas, N., Wagner, G., Kien, C., Klerings, I., and Gartlehner, G. (2018). Effects of lifestyle changes on adults with prediabetes: A systematic review and meta-analysis. Primary care diabetes, 12(5):393–408.
11 Kiran, M., Xie, Y., Anjum, N., Ball, G., Pierscionek, B., and Russell, D. (2025). Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis. Frontiers in Digital Health, 7:1557467.
12 Sledzik, R. and Zabihimayvan, M. (2022). Focal loss improves performance of high-sensitivity c-reactive protein imbalanced classification. In 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), pages 114–118.
13 Tuppad, A. and Devi Patil, S. (2024). An efficient classification framework for type 2 diabetes incorporating feature interactions. Expert Systems with Applications, 239:122138.