Ministério da Saúde: Guia de Orientações para o Método Canguru na Atenção Básica: Cuidado Compartilhado. Available at: https://bvsms.saude.gov.br/bvs/publicacoes/guia_orientacoes_metodo_canguru.pdf. accessed on: 10 April 2024 (2016)

UNICEF: Mortalidade Materna e na Infância - Mulheres e crianças estão sobrevivendo cada vez mais. Available at: https://shortlurl.com/3Ypr. accessed on: 15 June 2024 (2019)

World Health Organization: UNICEF-WHO Low Birthweight Estimates: Levels and Trends 2000-2015. Technical Report WHO/NMH/NHD/19.21, World Health Organization (2019)

World Health Organization: Feeding of very-low-birth-weight infants. https://www.who.int/tools/elena/interventions/feeding-vlbw-infants. accessed on: 14 june 2024 (2023)

Ministerio da Saúde: Ações Programáticas Estratégicas D. Gestação de alto risco: manual técnico. Available at: https://www.who.int/tools/elena/interventions/feeding-vlbw-infants. accessed on: 14 june 2024 (2022)

Lee, H.C., Liu, J., Profit, J., Hintz, S.R., Gould, J.B.: Survival without major morbidity among very low birth weight infants in california. Pediatrics 146(1) (2020). https://doi.org/10.1542/peds.2019-3865

Belfort GP, et al. Determinantes do baixo peso ao nascer em filhos de adolescentes: uma análise hierarquizada. Ciência & Saúde Coletiva. 2018;23:2609–20. https://doi.org/10.1590/1413-81232018238.13972016.

Article  Google Scholar 

Silva ER, Silva MGP. Perfil de recém-nascidos de baixo peso ao nascer no estado de pernambuco. Revista de Enfermagem Digital Cuidado e Promoção Saúde, Recife. 2021;3:1–6. https://doi.org/10.5935/2446-5682.20210011.

Article  Google Scholar 

Giovanola B, Tiribelli S. Beyond bias and discrimination: Redefining the ai ethics principle of fairness in healthcare machine-learning algorithms. AI & Society. 2023;38(2):549–63. https://doi.org/10.1007/s00146-022-01455-6.

Article  Google Scholar 

da Silva Rocha E, et al. On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Med Inform Decis Mak. 2022;22(1):334. https://doi.org/10.1186/s12911-022-02082-3.

Article  Google Scholar 

Freeman, I., Haigler, A., Schmeelk, S., Ellrodt, L., Fields, T.: What are they researching? examining industry-based doctoral dissertation research through the lens of machine learning. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1338–1340 (2018). IEEE

Breiman L. Random forests. Mach Learn. 2001;45(1):5–32. https://doi.org/10.1023/a:1010933404324.

Article  Google Scholar 

Ayyadevara, V.K.: Gradient boosting machine. In: Pro Machine Learning Algorithms, pp. 117–134. Springer, ??? (2018)

Cao Y, Miao Q-G, Liu J-C, Gao L. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica. 2013;39(6):745–58. https://doi.org/10.1016/s1874-1029(13)60052-x.

Article  Google Scholar 

Schapire, R.E.: Explaining adaboost. In: Empirical Inference, pp. 37–52. Springer, ??? (2013)

Lorena, A.C., Carvalho, A.C.P.L.F.D.: Uma introdução às support vector machines. Revista de Informática Teórica e Aplicada 14(2), 43–67 (2007). https://doi.org/10.22456/2175-2745.5690

Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565–7. https://doi.org/10.1038/nbt1206-1565.

Article  Google Scholar 

Bhavsar H, Ganatra A. A comparative study of training algorithms for supervised machine learning. International Journal of Soft Computing and Engineering (IJSCE). 2012;2(4):2231–307.

Google Scholar 

Zhang S, Cheng D, Deng Z, Zong M, Deng X. A novel k NN algorithm with data-driven k parameter computation. Pattern Recogn Lett. 2018;109:44–54. https://doi.org/10.1016/j.patrec.2017.09.036.

Article  Google Scholar 

Yang L, Shami A. On hyperparameter optimization of machine learning algorithms: Theory and practice. Neurocomputing. 2020;415:295–316.

Article  Google Scholar 

Snoek, J., Larochelle, H., Adams, R.P.: Practical bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

Mockus, J., Tiesis, V., Zilinskas, A.: The application of bayesian methods for seeking the extremum. In: Towards Global Optimization vol. 2, pp. 117–129 (1978)

Srinivas, N., Krause, A., Kakade, S., Seeger, M.: Gaussian process optimization in the bandit setting: No regret and experimental design. In: Proceedings of the 27th International Conference on Machine Learning (2010)

Gad AG. Particle swarm optimization algorithm and its applications: A systematic review. Archives of Computational Methods in Engineering. 2022;29:2531–61. https://doi.org/10.1007/s11831-021-09694-4.

Article  MathSciNet  Google Scholar 

Wang D, Tan D, Liu L. Particle swarm optimization algorithm: an overview. Soft Comput. 2018;22:387–408. https://doi.org/10.1007/s00500-016-2474-6.

Article  Google Scholar 

Ali A, Shamsuddin SM, Ralescu AL. Classification with class imbalance problem: A review. International Journal of Advanced Soft Computing and its Applications. 2015;7(3):176–204.

Google Scholar 

Kusuma WA, Rahmi AS, Heryanto R. Implementation of hybrid sampling technique for predicting active compound and protein interaction in unbalanced dataset. IOP Conference Series: Earth and Environmental Science. 2019;335:012005. https://doi.org/10.1088/1755-1315/335/1/012005.

Article  Google Scholar 

Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366:447–53. https://doi.org/10.1126/science.aax2342.

Article  Google Scholar 

Chung Y, et al. Automated data slicing for model validation: A big data-ai integration approach. IEEE Trans Knowl Data Eng. 2019;32(12):2284–96. https://doi.org/10.1109/TKDE.2019.2916074.

Article  Google Scholar 

Borson, N.S., Kabir, M.R., Zamal, Z., Rahman, R.M.: Correlation analysis of demographic factors on low birth weight and prediction modeling using machine learning techniques. In: 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 169–173 (2020). IEEE

Alabbad DA, Ajibi SY, Alotaibi RB, Alsqer NK, Alqahtani RA, Felemban NM, Rahman A, Aljameel SS, Ahmed MIB, Youldash MM. Birthweight range prediction and classification: A machine learning-based sustainable approach. Machine Learning and Knowledge Extraction. 2024;6(2):770–88.

Article  Google Scholar 

Cho H, Lee EH, Lee K-S, Heo JS. Machine learning-based risk factor analysis of adverse birth outcomes in very low birth weight infants. Sci Rep. 2022;12(1):12119.

Article  Google Scholar 

Zhang C, Yu X, Zhang B. Assessment of supervised longitudinal learning methods: Insights from predicting low birth weight and very low birth weight using prenatal ultrasound measurements. Comput Biol Med. 2024;182:109084.

Article  Google Scholar 

Shwartz-Ziv R, Armon A. Tabular data: Deep learning is not all you need. Information Fusion. 2022;81:84–90.

Article  Google Scholar 

Grinsztajn L, Oyallon E, Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? Adv Neural Inf Process Syst. 2022;35:507–20.

Google Scholar 

SINASC: Apresentação do SINASC. accessed on: 20 june 2024 (2024). https://svs.aids.gov.br/daent/cgiae/sinasc/apresentacao/

Gravena AAF, et al. Maternal age and factors associated with perinatal outcomes. ACTA Paulista de Enfermagem. 2013;26:130–5. https://doi.org/10.1590/S0103-21002013000200005.

Article  Google Scholar 

Attali E, Yogev Y. The impact of advanced maternal age on pregnancy outcome. Best Practice & Research Clinical Obstetrics & Gynaecology. 2021;70:2–9. https://doi.org/10.1016/j.bpobgyn.2020.06.006. (Epub 2020 Jun 24).

Article  Google Scholar 

Miao J, Niu L. A survey on feature selection. Procedia Computer Science. 2016;91:919–26. https://doi.org/10.1016/j.procs.2016.07.111.

Article  Google Scholar 

Kuhle S, Maguire B, Zhang H, et al. Comparison of logistic regression with machine learning methods for the prediction of fetal growth abnormalities: a retrospective cohort study. BMC Pregnancy Childbirth. 2018;18(1):333. https://doi.org/10.1186/s12884-018-1971-2.

Zahirzada, A., Lavangnananda, K.: Implementing predictive model for low birth weight in afghanistan. In: 2021 13th International Conference on Knowledge and Smart Technology (KST), Bangsaen, Chonburi, Thailand, pp. 67–72 (2021). https://doi.org/10.1109/KST51265.2021.9415792

Kader M, Perera NKP. Socio-economic and nutritional determinants of low birth weight in india. N Am J Med Sci. 2014;6(7):302–8. https://doi.org/10.4103/1947-2714.136902.

Article  Google Scholar 

Vyas DA, Eisenstein LG, Jones DS. Hidden in plain sight–reconsidering the use of race correction in clinical algorithms. N Engl J Med. 2020;383(9):874–82. https://doi.org/10.1056/NEJMms2004740.

Article  Google Scholar 

Noor, P.: Can we trust ai not to further embed racial bias and prejudice? BMJ (Clinical Research Edition) 368, 363 (2020). https://doi.org/10.1136/bmj.m363

Wu J, Zhu X, Lin Z, Ma W. Hyperparameter optimization for machine learning models based on bayesian optimization. Journal of Electronic Science and Technology. 2019;17(1):26–40. https://doi.org/10.11989/JEST.1674-862X.80904120.

Article  Google Scholar 

Gad AG. Particle swarm optimization algorithm and its applications: A systematic review. Archives of Computational Methods in Engineering. 2022;29:2531–61. https://doi.org/10.1007/s11831-021-09694-4.

Article  MathSciNet  Google Scholar 

de Geografia e Estatística, I.B.: Tabela 9605 - População residente, por cor ou raça, nos Censos Demográficos. Acessed on: 28 jun. 2024 (2024). https://sidra.ibge.gov.br/tabela/9605#resultado

Alt L, et al. O que é design thinking? Revista Coaching Brasil. 2017;213:14–7.

Google Scholar