Using the Fuzzy Triangular Naive Bayes to Assess Users inGynecological Examination Training
Abstract
This paper aimed to analyze the application of the Fuzzy Triangular
Naive Bayes method for assessment of users in a simulator for training in gynecological examination. This method is interesting for data that can be modeled by triangular statistical distribution. The method was analyzed with simulated data that represent two variables present in one of the stages of a gynecological examination. The performance of Fuzzy Triangular Naive Bayes are considered satisfactory when compared to a version that does not use fuzzy logic.
Keywords:
Fuzzy Triangular Naive Bayes, Online Asses, Virtual Reality, Gynecological Examination.
References
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Carcio, H. A. e Secor, R. M. (2018). Advanced health assessment of women: Clinical skills and procedures. Springer Publishing Company.
Ferreira, J. A., Soares, E. A., Machado, L. S., e Moraes, R. M. (2015). Assessment of fuzzy gaussian naive bayes for classification tasks. PATTERNS 2015, page 73.
Forbes, C., Evans, M., Hastings, N., e Peacock, B. (2011). Statistical distributions. John Wiley & Sons.
Gal, G. B., Weiss, E. I., Gafni, N., e Ziv, A. (2011). Preliminary assessment of faculty and student perception of a haptic virtual reality simulator for training dental manual dexterity. Journal of dental education, 75(4):496–504.
Hilden, M., Sidenius, K., Langhoff-Roos, J., Wijma, B., e Schei, B. (2003). Women’s experiences of the gynecologic examination: factors associated with discomfort. Acta obstetricia et gynecologica Scandinavica, 82(11):1030–1036.
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Machado, L. S. e Moraes, R. M. (2010). Intelligent decision making in training based on virtual reality. In Computational intelligence in complex decision systems, pages 85–123. Springer.
Machado, L. S. e Moraes, R. M. (2012). Assessment systems for training based on virtual reality: A comparison study. SBC Journal on Interactive Systems, 3(1):9–17.
Moraes, R. M. e Machado, L. S. (2014). Psychomotor skills assessment in medical training based on virtual reality using a weighted possibilistic approach. Knowledge-Based Systems, 70:97–102.
Moraes, R. M., Silva, I. L. A., e Machado, L. S. (2020). Online skills assessment in training based on virtual reality using a novel fuzzy triangular naive bayes network. In The 14th International FLINS Conference on Robotics and Artificial Intelligence and the 15th International Conference on Intelligent Systems and Knowledge Engineering (FLINS 2020). No prelo.
Paloc, C., Kitney, R., Bello, F., e Darzi, A. (2001). Virtual reality surgical training and assessment system. In International Congress Series, volume 1230, pages 210–217. Elsevier.
Ramoni, M. e Sebastiani, P. (2001). Robust bayes classifiers. Artificial Intelligence, 125(1-2):209–226.
Scalese, R. J., Obeso, V. T., e Issenberg, S. B. (2008). Simulation technology for skills training and competency assessment in medical education. Journal of general internal medicine, 23(1):46–49.
Soares, E. A. M. G. (2019). Fusion of online assessment methods for gynecological examination training. Master’s thesis, Mestrado em Modelos de Decisão e Saúde.
Soares, E. A. M. G. e Moraes, R. M. (2018). Fusion of online assessment methods for gynecological examination training: a feasibility study. TEMA (São Carlos), 19(3):423– 436.
Souza, D. F., Valdek, M. C., Moraes, R. M., e Machado, L. S. (2006). Siteg–sistema interativo de treinamento em exame ginecológico. In VIII Symposium on Virtual Reality SVR, volume 12.
Tori, R., Hounsell, M. d. S., e Kirner, C. (2018). Realidade virtual. Introdução a Readade Virtual e Aumentada.[Internet]. Porto Alegre: Editora SBC, pages 9–25.
Zadeh, L. A. (1968). Probability measures of fuzzy events. Journal of mathematical analysis and applications, 23(2):421–427.
Brasil (2013). Controle dos cânceres do colo do útero e da mama. Cadernos da Atenção Básica, Ministério da Saúde.
Burdea, G., Patounakis, G., Popescu, V., e Weiss, R. E. (1999). Virtual reality-based training for the diagnosis of prostate cancer. IEEE Transactions on Biomedical engineering, 46(10):1253–1260.
Carcio, H. A. e Secor, R. M. (2018). Advanced health assessment of women: Clinical skills and procedures. Springer Publishing Company.
Ferreira, J. A., Soares, E. A., Machado, L. S., e Moraes, R. M. (2015). Assessment of fuzzy gaussian naive bayes for classification tasks. PATTERNS 2015, page 73.
Forbes, C., Evans, M., Hastings, N., e Peacock, B. (2011). Statistical distributions. John Wiley & Sons.
Gal, G. B., Weiss, E. I., Gafni, N., e Ziv, A. (2011). Preliminary assessment of faculty and student perception of a haptic virtual reality simulator for training dental manual dexterity. Journal of dental education, 75(4):496–504.
Hilden, M., Sidenius, K., Langhoff-Roos, J., Wijma, B., e Schei, B. (2003). Women’s experiences of the gynecologic examination: factors associated with discomfort. Acta obstetricia et gynecologica Scandinavica, 82(11):1030–1036.
INCA (2020). Estimativa 2020 - incidência de câncer no brasil. 1.
Jannat, S. e Greenwood, A. G. (2012). Estimating parameters of the triangular distribution using nonstandard information. In Proceedings of the Winter Simulation Conference, pages 1–2.
Jerald, J. (2015). The VR book: Human-centered design for virtual reality. Morgan & Claypool.
Landis, J. R. e Koch, G. G. (1977). The measurement of observer agreement for categorical data. biometrics, pages 159–174.
Leserman, J. e Luke, C. (1982). An evaluation of an innovative approach to teaching the pelvic examination to medical students. Women & Health, 7(2):31–42.
Machado, L. S. e Moraes, R. M. (2010). Intelligent decision making in training based on virtual reality. In Computational intelligence in complex decision systems, pages 85–123. Springer.
Machado, L. S. e Moraes, R. M. (2012). Assessment systems for training based on virtual reality: A comparison study. SBC Journal on Interactive Systems, 3(1):9–17.
Moraes, R. M. e Machado, L. S. (2014). Psychomotor skills assessment in medical training based on virtual reality using a weighted possibilistic approach. Knowledge-Based Systems, 70:97–102.
Moraes, R. M., Silva, I. L. A., e Machado, L. S. (2020). Online skills assessment in training based on virtual reality using a novel fuzzy triangular naive bayes network. In The 14th International FLINS Conference on Robotics and Artificial Intelligence and the 15th International Conference on Intelligent Systems and Knowledge Engineering (FLINS 2020). No prelo.
Paloc, C., Kitney, R., Bello, F., e Darzi, A. (2001). Virtual reality surgical training and assessment system. In International Congress Series, volume 1230, pages 210–217. Elsevier.
Ramoni, M. e Sebastiani, P. (2001). Robust bayes classifiers. Artificial Intelligence, 125(1-2):209–226.
Scalese, R. J., Obeso, V. T., e Issenberg, S. B. (2008). Simulation technology for skills training and competency assessment in medical education. Journal of general internal medicine, 23(1):46–49.
Soares, E. A. M. G. (2019). Fusion of online assessment methods for gynecological examination training. Master’s thesis, Mestrado em Modelos de Decisão e Saúde.
Soares, E. A. M. G. e Moraes, R. M. (2018). Fusion of online assessment methods for gynecological examination training: a feasibility study. TEMA (São Carlos), 19(3):423– 436.
Souza, D. F., Valdek, M. C., Moraes, R. M., e Machado, L. S. (2006). Siteg–sistema interativo de treinamento em exame ginecológico. In VIII Symposium on Virtual Reality SVR, volume 12.
Tori, R., Hounsell, M. d. S., e Kirner, C. (2018). Realidade virtual. Introdução a Readade Virtual e Aumentada.[Internet]. Porto Alegre: Editora SBC, pages 9–25.
Zadeh, L. A. (1968). Probability measures of fuzzy events. Journal of mathematical analysis and applications, 23(2):421–427.
Published
2020-10-20
How to Cite
SILVA, Ingrid; SOARES, Elaine; MACHADO, Liliane; MORAES, Ronei.
Using the Fuzzy Triangular Naive Bayes to Assess Users inGynecological Examination Training. In: NATIONAL MEETING ON ARTIFICIAL AND COMPUTATIONAL INTELLIGENCE (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 258-269.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12134.
