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English Information

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Authors
# Name
1 Luis Enrique Zarate(zarate@pucminas.br)
2 Anna Silva(annaluizabh10@gmail.com)

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Reference
# Reference
1 Zafar, S., Khurram, H., Kamran, M., Fatima, M., Parvaiz, A., and Shaikh, R. S. (2023). Potential of gja8 gene variants in predicting age-related cataract: A comparison of supervised machine learning methods. PLOS One.
2 Tomoyo, Y., Akiko, H., Kazumasa, Y., Kenya, Y., Miki, U., Yoko, O., Mariko, S., Kazuo,T., Norie, S., Kazuno, N., Shoichiro, T., and Hiroyasu, I. (2021). Hypertension and hypercholesterolemia are associated with cataract development in patients with type 2 diabetes. High Blood Press Cardiovasc Prev, 28:475–481.
3 Santhanam, P. and Ahima, R. (2019). Machine learning and blood pressure. J Clin Hypertens.
4 Ranran, C., Jinming, L., Yujie, L., Yiping, J., Xue, W., Hong, L., Yanlong, B., and Haohao, Z. (2024). Machine learning models for predicting 24-hour intraocular pressure changes: A comparative study. Med Sci Monit., 3(30).
5 Nunez, R., Harris, A., Szopos, M., Rai, R., Keller, J., Wikle, C., Robinson, E. L., Lin, M., Zou, D., Verticchio, A., Siesky, B. A., and Guidoboni, G. (2022). Clarifying the roles of high and low blood pressure in glaucoma via physiology-informed machine learning. Invest. Ophthalmol. Vis. Sci., 63(7).
6 Lin, D., Chen, J., Lin, Z., Li, X., Zhang, K., Wu, X., Liu, Z., Huangc, J., Li, J., Zhu, Y., Chen, C., Zhao, L., Xiang, Y., Guo, C., Wang, L., Liu, Y., Chen, W., and Lin, H. (2020). A practical model for the identification of congenital cataracts using machine learning. eBioMedicine
7 Kuriakose, K. K., Raj, B., Murty, S., and Swaminathan, P. (2010). Knowledge management maturity models – a morphological analysis. Journal of Knowledge Management Practice, 11(3):1–10.
8 Ishii, K., Ryo, A., Takashi, O., Shingo, M., Yuri, F., Hiroshi, M., Keiichi, O., Atsushi, N., Shuhei, Y., Akira, O., and Masaki, T. (2021). Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort. Sci Rep, 11.
9 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
10 Hirohiko, K., Koshimizu, H., Nakamura, K., and Okuno, Y. (2024). Recent developments in machine learning modeling methods for hypertension treatment. Hypertension Research, 47(3):700–707
11 Gonçalves, L., Franca, D., and Zarate, 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.
12 Ang, M. J. and Afshari, N. A. (2021). Cataract and systemic disease: A review. Clinical & Experimental Ophthalmology, 49(2):118–127.