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 Gustavo Costa(gustavocosta.ds09@gmail.com)
2 Luis Enrique Zarate(zarate@pucminas.br)

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

Reference
# Reference
1 AlKaabi, L., Ahmed, L., Al Attiyah, M., and Abdel-Rahman, M. (2020). Predicting hypertension using machine learning: Findings from qatar biobank study. PLOS ONE, 15(10):e0240370.
2 Alwan, A. (2011). Global status report on noncommunicable diseases 2010. World Health Organization, Geneva. 176 pp.
3 Bhatt, C., Patel, P., Ghetia, T., and Mazzeo, P. (2023). Effective heart disease prediction using machine learning techniques. Algorithms, 16(2):88.
4 de Araújo, J., de Alencar Rodrigues, R., da Costa Pereira de Arruda Neta, A., et al. (2022). The direct and indirect costs of cardiovascular diseases in brazil. PLOS ONE, 17(12):e0278891.
5 de Carvalho, N., Gomes, M., and Zárate, L. (2024). Mineração de dados no diagnóstico de hipertensão baseado na pesquisa nacional em saúde 2019. J Health Inform, 16(Especial)
6 Gárate-Escamila, A., El Hassani, A., and Andrès, E. (2020). Classification models for heart disease prediction using feature selection and pca. Informatics in Medicine Unlocked, 19:100330.
7 Gonçalves, L., Franca, D., and Zárate, L. (2024). Relevância do entendimento do domínio de problema na construção de modelos computacionais de aprendizado. In Anais do XVIII Brazilian e-Science Workshop, pages 135–142, Porto Alegre, RS, Brasil. SBC.
8 IBGE (2020). Pesquisa nacional de saúde 2019- instituto brasileiro de geografia e estatística. https://www.ibge.gov.br/estatisticas/sociais/ saude/9160-pesquisa-nacional-de-saude.html?edicao=25921& t=resultados. Acesso em: 2024-07-15.
9 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.
10 Malta, D. et al. (2022). Hipertensão arterial e fatores associados: Pesquisa nacional de saúde, 2019. Revista de Saúde Pública, 56:122.
11 National Institute on Alcohol Abuse and Alcoholism (2022). Standard alcohol guidelines.
12 Powell-Wiley, T., Poirier, P., Burke, L., et al. (2021). Obesity and cardiovascular disease: Ascientific statement from the american heart association. Circulation, 143(21):e84 e118.
13 Sousa, C., Ribeiro, A., Barreto, S., et al. (2022). Diferenças raciais no controle da pressão arterial em usuários de anti-hipertensivos em monoterapia: resultados do estudo elsabrasil. Arq. Bras. Cardiol., 118(3):614–622.
14 Sousa, M. and Zarate, L. (2024). A epidemia silenciosa: Explorando os determinantes comportamentais e socioeconômicos da deficiência renal crônica no brasil. In Anais Estendidos do XXXIX Simpósio Brasileiro de Bancos de Dados, pages 318–327, Porto Alegre, RS, Brasil. SBC
15 Stevens, B., Pezzullo, L., Verdian, L., Tomlinson, J., George, A., and Bacal, F. (2018). The economic burden of heart conditions in brazil. Arq. Bras. Cardiol., 111(1):29–36.
16 WHO(2011). Global Atlas on Cardiovascular Disease Prevention and Control. World Health Organization, Geneva.
17 WHO(2021). Obesity and overweight.
18 Yang, J., Rahardja, S., and Franti, P. (2019). Outlier detection: how to threshold outlier scores? In Proc. of the Int. Conf. on Artificial Intelligence, Information Processing and Cloud Computing, pages 37–42.
19 Zilbermint, M., Hannah-Shmouni, F., and Stratakis, C. (2019). Genetics of hypertension in african americans and others of african descent. Int. J. Mol. Sci., 20(5):1081.