Planejamento Cirúrgico de Estrabismo Horizontal Utilizando Árvore de Regressão de Múltiplas Saídas
Resumo
O estrabismo e uma patologia oftalmológica que afeta entre 1,55% e 3,6% da população, que pode causar danos sensoriais irreversíveis à visão. O tratamento dos casos mais graves requer intervenção cirúrgica. Desta forma, este trabalho utiliza Árvores de Regressão aplicadas em forma de única saída e múltiplas saídas para indicar o planejamento de cirurgias de estrabismo. Em nosso método mais preciso, Multi-Target Regression Trees, em uma abordagem em única saída, foi obtido o Erro Absoluto Médio de 0,49 milímetros, a Raiz do Erro Quadrático Médio de 1,49 milímetros e R2 de 0,65 na indicação do plano cirúrgico de estrabismos horizontais.
Referências
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De’Ath, G. (2002). Multivariate regression trees: a new technique for modeling species– environment relationships. Ecology, 83(4):1105–1117.
Foschini, R. M. and Bicas, H. E. (2001). Cirurgias de musculos retos horizontais: análise de planejamentos e resultados. Arquivos Brasileiros de Oftalmologia, 64(6):523–534. Haryadi, S. (2017). The non-intercept linear regression method. Researchgate. DOI, 10.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer.
Kasuya, E. (2019). On the use of r and r squared in correlation and regression. Ecological Research, 34(1):235–236.
Li, J. (2018). Monthly housing rent forecast based on lightgbm (light gradient boosting) model. International Journal of Intelligent Information and Management Science, 7(6).
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Omurlu, I. K., Ture, M., Unubol, M., Katranci, M., and Guney, E. (2014). Comparing performances of logistic regression, classification & regression trees and artificial neural networks for predicting albuminuria in type 2 diabetes mellitus. Int J Sci Basic Appl Res, 16(1):173–87.
Osojnik, A., Panov, P., and Dzeroski, S. (2017). Multi-label classification via multi-targetˇ regression on data streams. Machine Learning, 106(6):745–770.
Ozates, S., Ozates, M. E., Can, C. U., Polat, S., Yasar, H. H., Taskale, B., and Gogus, A. K. (2019). Improvement in psychiatric symptoms after strabismus surgery in adolescent patients in long-term follow-up. British Journal of Ophthalmology, 103(7):966–970.
Paper, D. and Paper, D. (2020). Scikit-learn regression tuning. Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, pages 189–213.
Petkovic, M., D zeroski, S., and Kocev, D. (2017). Feature ranking for multi-target regres-ˇ sion with tree ensemble methods. In International Conference on Discovery Science, pages 171–185. Springer.
Potdar, K., Pardawala, T. S., and Pai, C. D. (2017). A comparative study of categorical variable encoding techniques for neural network classifiers. International journal of computer applications, 175(4):7–9.
Repka, M. X., Lum, F., and Burugapalli, B. (2018). Strabismus, strabismus surgery, and reoperation rate in the united states: analysis from the iris registry. Ophthalmology, 125(10):1646–1653.
Rocha, M. N. A. M. (2016). Propostas de algoritmos e formulas para planejamento cirurgico dos estrabismos horizontais.
Rolli Khurana, N. S., Srivastava, R. M., and Agrawal, S. (2018). Planning strabismus surgery. Strabismus: For every Ophthalmologist, page 115.
Schutte, S., Polling, J. R., van der Helm, F. C., and Simonsz, H. J. (2009). Human error in strabismus surgery: quantification with a sensitivity analysis. Graefe’s Archive for Clinical and Experimental Ophthalmology, 247(3):399.
Sharma, P., Gaur, N., Phuljhele, S., and Saxena, R. (2017). What’s new for us in strabismus? Indian journal of ophthalmology, 65(3):184.
Silva, T. A., de Almeida, J. D. S., Teixeira, J. A. M., and Junior, G. B. (2017). Planejamento cirurgico de estrabismo horizontal utilizando regressores de múltiplas sa ídas. In Anais do XVII Workshop de Informatica Médica . SBC.
Witten, I. H. and Frank, E. (2002). Data mining: practical machine learning tools and techniques with java implementations. Acm Sigmod Record, 31(1):76–77.
Bastı, E., Kuzey, C., and Delen, D. (2015). Analyzing initial public offerings’ short-term performance using decision trees and svms. Decision Support Systems, 73:15–27.
Borchani, H., Varando, G., Bielza, C., and Larranaga, P. (2015). A survey on multi-output regression. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5):216–233.
Breskvar, M., Kocev, D., and Dzeroski, S. (2018). Ensembles for multi-target regressionˇ with random output selections. Machine Learning, 107(11):1673–1709.
de Almeida, J. D. S., Silva, A. C., Teixeira, J. A. M., Paiva, A. C., and Gattass, M. (2015). Surgical planning for horizontal strabismus using support vector regression. Computers in biology and medicine, 63:178–186.
De’Ath, G. (2002). Multivariate regression trees: a new technique for modeling species– environment relationships. Ecology, 83(4):1105–1117.
Foschini, R. M. and Bicas, H. E. (2001). Cirurgias de musculos retos horizontais: análise de planejamentos e resultados. Arquivos Brasileiros de Oftalmologia, 64(6):523–534. Haryadi, S. (2017). The non-intercept linear regression method. Researchgate. DOI, 10.
James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer.
Kasuya, E. (2019). On the use of r and r squared in correlation and regression. Ecological Research, 34(1):235–236.
Li, J. (2018). Monthly housing rent forecast based on lightgbm (light gradient boosting) model. International Journal of Intelligent Information and Management Science, 7(6).
Ma, X. (2018). Using classification and regression trees: A practical primer. IAP.
Omurlu, I. K., Ture, M., Unubol, M., Katranci, M., and Guney, E. (2014). Comparing performances of logistic regression, classification & regression trees and artificial neural networks for predicting albuminuria in type 2 diabetes mellitus. Int J Sci Basic Appl Res, 16(1):173–87.
Osojnik, A., Panov, P., and Dzeroski, S. (2017). Multi-label classification via multi-targetˇ regression on data streams. Machine Learning, 106(6):745–770.
Ozates, S., Ozates, M. E., Can, C. U., Polat, S., Yasar, H. H., Taskale, B., and Gogus, A. K. (2019). Improvement in psychiatric symptoms after strabismus surgery in adolescent patients in long-term follow-up. British Journal of Ophthalmology, 103(7):966–970.
Paper, D. and Paper, D. (2020). Scikit-learn regression tuning. Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python, pages 189–213.
Petkovic, M., D zeroski, S., and Kocev, D. (2017). Feature ranking for multi-target regres-ˇ sion with tree ensemble methods. In International Conference on Discovery Science, pages 171–185. Springer.
Potdar, K., Pardawala, T. S., and Pai, C. D. (2017). A comparative study of categorical variable encoding techniques for neural network classifiers. International journal of computer applications, 175(4):7–9.
Repka, M. X., Lum, F., and Burugapalli, B. (2018). Strabismus, strabismus surgery, and reoperation rate in the united states: analysis from the iris registry. Ophthalmology, 125(10):1646–1653.
Rocha, M. N. A. M. (2016). Propostas de algoritmos e formulas para planejamento cirurgico dos estrabismos horizontais.
Rolli Khurana, N. S., Srivastava, R. M., and Agrawal, S. (2018). Planning strabismus surgery. Strabismus: For every Ophthalmologist, page 115.
Schutte, S., Polling, J. R., van der Helm, F. C., and Simonsz, H. J. (2009). Human error in strabismus surgery: quantification with a sensitivity analysis. Graefe’s Archive for Clinical and Experimental Ophthalmology, 247(3):399.
Sharma, P., Gaur, N., Phuljhele, S., and Saxena, R. (2017). What’s new for us in strabismus? Indian journal of ophthalmology, 65(3):184.
Silva, T. A., de Almeida, J. D. S., Teixeira, J. A. M., and Junior, G. B. (2017). Planejamento cirurgico de estrabismo horizontal utilizando regressores de múltiplas sa ídas. In Anais do XVII Workshop de Informatica Médica . SBC.
Witten, I. H. and Frank, E. (2002). Data mining: practical machine learning tools and techniques with java implementations. Acm Sigmod Record, 31(1):76–77.
Publicado
15/09/2020
Como Citar
LEITE, Fernando; ALMEIDA, João; TEIXEIRA, Jorge; CRUZ, Luana; JUNIOR, Geraldo; PAIVA, Anselmo.
Planejamento Cirúrgico de Estrabismo Horizontal Utilizando Árvore de Regressão de Múltiplas Saídas. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 356-367.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11527.
