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English Information

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Authors
# Name
1 Ligia Gonçalves(ligiacarv.goncalves@gmail.com)
2 Luis Zarate(zarate@pucminas.br)

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Reference
# Reference
1 Dauchet, L., Amouyel, P., Hercberg, S., & Dallongeville, J. (2005). Fruit and vegetable consumption and risk of coronary heart disease: A meta-analysis of cohort studies. The Journal of Nutrition, 135(10), 2589-2593.
2 Dritsas, E., & Trigka, M. (2022). Stroke Risk Prediction with Machine Learning Techniques. Sensors (Basel), 22(13), 4670. doi: 10.3390/s22134670. PMID: 35808172; PMCID: PMC9268898.
3 Howard, George et al. “Age-Related Differences in the Role of Risk Factors for Ischemic Stroke.” Neurology vol. 100,14 (2023): e1444-e1453. doi:10.1212/WNL.0000000000206837
4 Malik, V. S., Schulze, M. B., & Hu, F. B. (2010). Intake of sugar-sweetened beverages and weight gain: a systematic review. The American Journal of Clinical Nutrition, 84(2), 274-288.
5 Mozaffarian, D., & Rimm, E. B. (2006). Fish intake, contaminants, and human health: evaluating the risks and the benefits. JAMA, 296(15), 1885-1899.
6 Noche, Rommell B. et al. “Abstract 156: Recurrent Stroke in Middle-Aged Lacunar Stroke Survivors: Understanding Risk Factors and Vulnerability in an Important Target Population.” Stroke (2020): n. pag.
7 Paixão, Gabriela Miana de Mattos et al. “Machine Learning in Medicine: Review and Applicability.” “Machine Learning na Medicina: Revisão e Aplicabilidade.” Arquivos brasileiros de cardiologia vol. 118,1 (2022): 95-102. doi:10.36660/abc.20200596
8 Rajati, F., Rajati, M., Rasulehvandi, R., & Kazeminia, M. (2023). Prevalence of stroke in the elderly: A systematic review and meta-analysis. Interdisciplinary Neurosurgery, 32, 101746, ISSN 2214-7519. doi: 10.1016/j.inat.2023.101746.
9 Yousufuddin, M., & Young, N. (2019). Aging and ischemic stroke. Aging (Albany NY), 11(9), 2542-2544. doi: 10.18632/aging.101931. PMID: 31043575; PMCID: PMC6535078.
10 Zárate, L., Petrocchi, B., Dias, M. C., Felix, C., & Gomes, M. (2023). CAPTO - A method for understanding problem domains for data science projects: CAPTO - Um método para entendimento de domínio de problema para projetos em ciência de dados. Concilium, 23, 922-941. doi: 10.53660/CLM-1815-23M33.