Evaluating how different balancing data techniques impact on prediction of premature birth - Extended Abstract – CTDGSI 2025
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Este artigo não possui um resumo.
Referências
Akazawa, M. and Hashimoto, K. (2022). Prediction of preterm birth using artificial intelligence: a systematic review. Journal of Obstetrics and Gynaecology, 42(6):1662–1668.
Auger, Nathaliea, b. c. D. P. H. S. P. R. W. (2012). Estimating gestational-age-specific and cause-specific associations. Epidemiology 23(2).
Fuchs, F., Monet, B., Ducruet, T., Chaillet, N., and Audibert, F. (2018). Effect of maternal age on the risk of preterm birth: A large cohort study. PLOS ONE, 13(1):1–10.
Lee, K.-S. and Ahn, K. H. (2020). Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth. Diagnostics, 10(9).
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., and Scheinost, D. (2024). Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications, 15(1):1829.
Schempf, A. H., Branum, A. M., Lukacs, S. L., and Schoendorf, K. C. (2007). Maternal age and parity-associated risks of preterm birth: differences by race/ethnicity. Paediatric and Perinatal Epidemiology, 21(1):34–43.
Silva, A. B., Rocha, E. d. S., Lorenzato, J. F., and Endo, P. T. (2025). Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models. PloS one, 20(3):e0316574.
Waldenström, U., Cnattingius, S., Vixner, L., and Norman, M. (2017). Advanced maternal age increases the risk of very preterm birth, irrespective of parity: a population-based register study. BJOG: An International Journal of Obstetrics & Gynaecology, 124(8):1235–1244.
WHO (2023). Who. preterm birth.
Auger, Nathaliea, b. c. D. P. H. S. P. R. W. (2012). Estimating gestational-age-specific and cause-specific associations. Epidemiology 23(2).
Fuchs, F., Monet, B., Ducruet, T., Chaillet, N., and Audibert, F. (2018). Effect of maternal age on the risk of preterm birth: A large cohort study. PLOS ONE, 13(1):1–10.
Lee, K.-S. and Ahn, K. H. (2020). Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth. Diagnostics, 10(9).
Rosenblatt, M., Tejavibulya, L., Jiang, R., Noble, S., and Scheinost, D. (2024). Data leakage inflates prediction performance in connectome-based machine learning models. Nature Communications, 15(1):1829.
Schempf, A. H., Branum, A. M., Lukacs, S. L., and Schoendorf, K. C. (2007). Maternal age and parity-associated risks of preterm birth: differences by race/ethnicity. Paediatric and Perinatal Epidemiology, 21(1):34–43.
Silva, A. B., Rocha, E. d. S., Lorenzato, J. F., and Endo, P. T. (2025). Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models. PloS one, 20(3):e0316574.
Waldenström, U., Cnattingius, S., Vixner, L., and Norman, M. (2017). Advanced maternal age increases the risk of very preterm birth, irrespective of parity: a population-based register study. BJOG: An International Journal of Obstetrics & Gynaecology, 124(8):1235–1244.
WHO (2023). Who. preterm birth.
Publicado
19/05/2025
Como Citar
SILVA, Anna Beatriz; ROCHA, Elisson da Silva; ENDO, Patricia Takako.
Evaluating how different balancing data techniques impact on prediction of premature birth - Extended Abstract – CTDGSI 2025. In: CONCURSO DE TESES, DISSERTAÇÕES E TCCS EM SISTEMAS DE INFORMAÇÃO - SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 21. , 2025, Recife/PE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 111-114.
DOI: https://doi.org/10.5753/sbsi_estendido.2025.246727.
