Abstract
Warts and condylomas are benign skin proliferation caused by HPV (human papillomavirus), which can appear anywhere in the body, including the genital regions. One of the treatments used to combat this type of tumor is immunotherapy, a technique that advances the stimulation of the immune system through the use of substances that modify the biological response in human beings. Immunological reactions may be the result of the antigen-antibody interaction or the mechanisms involved in cell-mediated immunity. Health professionals working with these techniques can use specialist systems to assist in the diagnosis of treatment effectiveness in patients. A system based on fuzzy logic was developed with data from medical research. This system can predict the adaptability of a patient to the treatment with 83.33% accuracy. This article proposes the use of a hybrid model of artificial intelligence and fuzzy logic to improve the predictive results of the expert system through the creation of fuzzy rules to construct a more interpretative expert system. Based on the tests performed, we can infer that the proposed model kept the results statistically equal in the prediction of efficiency in the immunotherapeutic treatment, besides making possible the creation of fuzzy rules based on the data of the research on the medication.
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References
Mandelblatt, J.S., et al.: Benefits and costs of using HPV testing to screen for cervical cancer. Jama 287(18), 2372–2381 (2002)
Marianelli, R., Nadal, S.R.: Utilidade da citologia anal no rastreamento dos homens heterossexuais portadores do HPV genital Anal cytology for screening heterosexual men harboring genital HPV infection. Rev. Bras. Coloproctol. 30(3), 365–367 (2010)
Khozeimeh, F., Alizadehsani, R., Roshanzamir, M., Khosravi, A., Layegh, P., Nahavandi, S.: An expert system for selecting wart treatment method. Comput. Biol. Med. 81, 167–175 (2017)
Bakirtzis, A.G., Theocharis, J.B., Kiartzis, S.J., Satsios, K.J.: Short term load forecasting using fuzzy neural networks. IEEE Trans. Power Syst. 10(3), 1518–1524 (1995)
Kasabov, N.: Evolving fuzzy neural networks-algorithms, applications and biological motivation. Methodol. Concept. Des. Appl. Soft Comput. World Sci. 1, 271–274 (1998)
Özbay, Y., Ceylan, R., Karlik, B.: A fuzzy clustering neural network architecture for classification of ECG arrhythmias. Comput. Biol. Med. 36(4), 376–388 (2006)
Silva, A.M., Caminhas, W.M., Lemos, A.P., Gomide, F.: Evolving neo-fuzzy neural network with adaptive feature selection. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC), pp. 341–349. IEEE, September 2013
Souza, P.V.C., Torres, L.C.B.: Regularized fuzzy neural network based on or neuron for time series forecasting. In: Proceedings of 37th North American Fuzzy Information Processing Society Annual Conference (NAFIPS 2018), Fortaleza (2018)
Ballini, R., Soares, S., Andrade, M.G.: PrevisĂ£o de vazões mĂ©dias mensais usando redes neurais nebulosas. SBA: Controle AutomaĂ§Ă£o Sociedade Brasileira de Automatica 14(3), 680–693 (2003)
Souza, P.V.C.: DetecĂ§Ă£o de pulsares utilizando redes neurais nebulosas baseadas em uninormas. In: Quinto Congresso Brasileiro de Sistemas Fuzzy? V CBSF, 2018, Fortaleza. Anais do Quinto Congresso Brasileiro de Sistemas Fuzzy. V CBSF (2018)
Souza, P.V.C.: Regularized fuzzy neural networks for pattern classification problems. Int. J. Appl. Eng. Res. 13(5), 2985–2991 (2018)
Souza, P.V.C., Silva, G.R.L., Torres, L.C.B.: Uninorm based regularized fuzzy neural network. In: IEEE Technical Committee on Evolving and Adaptive Intelligent Systems, Proceedings 2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS 2018) 2018. SMC Society and the IEEE Computation, Rhodes/Greece, Greece (2018)
Khozeimeh, F., et al.: Intralesional immunotherapy compared to cryotherapy in the treatment of warts. Int. J. Dermatol. 56(4), 474–478 (2017)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Muñoz, N., et al.: Epidemiologic classification of human papillomavirus types associated with cervical cancer. N. Engl. J. Med. 348(6), 518–527 (2003)
Scheinfeld, N., Lehman, D.S.: An evidence-based review of medical and surgical treatments of genital warts. Dermatol. Online J. 12(3), 5 (2006)
How Does Immunotherapy Work? https://www.roswellpark.org/immunotherapy/about-immunotherapy/how-does-immunotherapy-work. Accessed 31 May 2018
Alvarez-Cuesta, E., Bousquet, J., Canonica, G.W., Durham, S.R., Malling, H.J., Valovirta, E.: Standards for practical allergen-specific immunotherapy. Allergy 61, 1–3 (2006)
Panza, F., et al.: Immunotherapy for Alzheimer’s disease: from anti-β-amyloid to tau-based immunization strategies. Immunotherapy 4(2), 213–238 (2012)
Majid, I., Imran, S.: Immunotherapy with intralesional Candida albicans antigen in resistant or recurrent warts: a study. Indian J. Dermatol. 58(5), 360 (2013)
Haykin, S., Network, N.: A comprehensive foundation. Neural Netw. 2(2004), 41 (2004)
de Oliveira, A.C.S., de Souza, A.A., Lacerda, W.S., Gonçalves, L.R.: AplicaĂ§Ă£o de redes neurais artificiais na previsĂ£o da produĂ§Ă£o de Ă¡lcool (2010)
Calvo, R.: Arquitetura hĂbrida inteligente para navegaĂ§Ă£o autĂ´noma de robĂ´s (Doctoral dissertation, Universidade de SĂ£o Paulo) (2007)
https://www.researchgate.net/publication/221908927_Development_of_Fuzzy-Logic-Based_Self_Tuning_PI_Controller_for_Servomotor/figures?lo=1&utm_source=google&utm_medium=organic. Accessed 31 May 2018
Pedrycz, W.: Processing in relational structures: fuzzy relational equations. Fuzzy Sets Syst. 40(1), 77–106 (1991)
Jang, J.S.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Lemos, A., Caminhas, W., Gomide, F.: New uninorm-based neuron model and fuzzy neural networks. In: Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American, pp. 1–6. IEEE, July 2010
Bach, F.R.: Bolasso: model consistent lasso estimation through the bootstrap. In: Proceedings of the 25th international conference on Machine learning, pp. 33–40. ACM, July 2008
UCI Machine Learning- Immunotherapy Dataset Data Set https://archive.ics.uci.edu/ml/datasets/Immunotherapy+Dataset#. Accessed 31 May 2018
Guttenberg, N., Kanai, R.: Learning to generate classifiers. arXiv preprint arXiv:1803.11373 (2018)
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GuimarĂ£es, A.J., Silva Araujo, V.J., de Campos Souza, P.V., Araujo, V.S., Rezende, T.S. (2018). Using Fuzzy Neural Networks to the Prediction of Improvement in Expert Systems for Treatment of Immunotherapy. In: Simari, G., FermĂ©, E., GutiĂ©rrez Segura, F., RodrĂguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_19
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