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 Lívia Ruback(liviacr@unicamp.br)
2 Luisa Felix(luisa.felix.oliveira@gmail.com)
3 Ariel Soares Teles(ariel.teles@ifma.edu.br)

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

Reference
# Reference
1 Association, A. D. (2023). Standards of medical care in diabetes. Dia- betes Care, 46(Supplement 1):S1–S291.
2 Bhatti, A., Chen, L., and Kumar, R. (2025). The influence of missing data mechanisms and simple missing data handling techniques on fairness. Journal of Fairness in Machine Learning. In press.
3 Blow, C. H., Qian, L., Gibson, C., Obiomon, P., and Dong, X. (2024). Comprehensive validation on reweighting samples for bias mitigation via aif360. Applied Sciences, 14(9):3826.
4 Caton, S. and Haas, C. (2024). Fairness in machine learning: A survey. ACM Comput. Surv., 56(7).
5 Chang, V., Ganatra, M. A., Hall, K., Golightly, L., and Xu, Q. A. (2022). An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators. Healthcare Analytics, 2:100118.
6 Cronjé, H., Katsiferis, A., Elsenburg, L., Andersen, T., Rod, N., et al. (2023). Assessing racial bias in type 2 diabetes risk prediction algorithms. PLOS Global Public Health, 3(5):e0001556.
7 Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R. S. (2012). Fairness through awareness. In Goldwasser, S., editor, Proceedings of the 3rd Innovations in Theoretical Computer Science Conference on - ITCS '12, pages 214–226. ACM.
8 Diabetes Collaborators 2023] GBD 2021 Diabetes Collaborators (2023). Global, regional, and national burden of diabetes from 1990 to 2021: a systematic analysis for the global burden of disease study 2021. The Lancet, 402(10397):203–234.
9 Hardt, M., Price, E., and Srebro, N. (2016). Equality of opportunity in supervised learning. In Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS’16, page 3323–3331, Red Hook, NY, USA. Curran Associates Inc.
10 Huang, J., Galal, G., Etemadi, M., and Vaidyanathan, M. (2022). Evalu- ation and mitigation of racial bias in clinical machine learning models: Scoping review. JMIR Med Inform, 10(5):e36388.
11 Huang, Y., Guo, J., Chen, W.-H., Lin, H.-Y., Tang, H., Wang, F., Xu, H., and Bian, J. (2024). A scoping review of fair machine learning techniques when using real-world data. medRxiv
12 Jain, H. P., Patel, S. R., and Joshi, M. V. (2022). Addressing bias in dia- betes prediction models through fairness-aware learning. Journal of Ambient Intelligence and Humanized Computing, 13(2):1085–1098.
13 Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. (2012). Fairness-aware classifier with prejudice remover regularizer. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2012), pages 35–50. Springer.
14 Khanam, R., Sultana, M., Rahman, M. M., and Ahmed, M. (2021). A comparison of machine learning algorithms for diabetes prediction. International Journal of Engineering Research and Technology, 10(3):1–6.
15 Klement, W. and El Emam, K. (2023). Consolidated report- ing guidelines for prognostic and diagnostic machine learning modeling studies: Development and validation. J Med Internet Res, 25:e48763.
16 Introducing ai fairness 360. https://research.ibm.com/blog/ai-fairness-360. Accessed: 2025-04-27.
17 Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., and Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Comput. Surv., 54(6).
18 Pias, T. S., Su, Y., Tang, X., Wang, H., Faghani, S., and Yao, D. D. (2023). Enhancing fairness and accuracy in type 2 diabetes prediction through data resampling. medRxiv.
19 Raza, S. (2022). A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission. Healthcare Analytics, 2:100100.
20 Raza, S. (2023). Connecting fairness in machine learning with public health equity.
21 Ruback, L., Carvalho, D., and Avila, S. (2022). Mitigating bias in machine learning: A socio-technical analysis. iSys - Brazilian Journal of Information Systems, 15(1):23:1–23:31.
22 Sajid, M. R., Alkhawaldeh, R. S., Hafez, A. M., and Alkhawaldeh, M. (2024). Predicting diabetes in adults: identifying important features in unbalanced data over a 5-year cohort study using machine learning algorithm. BMC Medical Research Methodology, 24(1):211.
23 Varshney, K. R. (2022). Trustworthy Machine Learning. Independently Published, Chappaqua, NY, USA.
24 Verma, S. and Rubin, J. (2018). Fairness definitions explained. In Proceedings of the International Workshop on Software Fairness, FairWare ’18, page 1–7, New York, NY, USA. Association for Computing Machinery.
25 Wang, S. C. Y., Nickel, G., Venkatesh, K. P., Raza, M. M., and Kvedar, J. C. (2024). Ai-based diabetes care: risk prediction models and implementation concerns. NPJ Digital Medicine, 7(1):36
26 Wright, R. E. (1995). Logistic regression.
27 Xie, Z., Nikolayeva, O., Luo, J., and Li, D. (2019). Building risk prediction models for type 2 diabetes using machine learning techniques. Preventing Chronic Disease, 16:E130.