Abstract
Artificial neural networks are being used in diagnosis support systems to detect different kind of diseases. As the design of multilayer perceptron is an open question, the present work shows a comparison between a traditional empirical way and neuroevolution method to find the best architecture to solve the disease detection problem. Tuberculosis and appendicitis databases were employed to test both proposals. Results show that neuroevolution offers a good alternative for the tuberculosis problem but there is lacks of performance in the appendicitis one.
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Acknowledgment
Authors thank the Universidad Antonio Nariño under project 2016207, University of Connecticut, Universidad Santo Tomas and Universidade Estadual do Rio de Janeiro for the support and financial assistance in this work. Also, the Hospital Santa Clara and Carlos Awad for making available the database related with tuberculosis.
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Orjuela-Cañón, A.D., Posada-Quintero, H.F., Valencia, C.H., Mendoza, L. (2018). On the Use of Neuroevolutive Methods as Support Tools for Diagnosing Appendicitis and Tuberculosis. In: Figueroa-García, J., López-Santana, E., Rodriguez-Molano, J. (eds) Applied Computer Sciences in Engineering. WEA 2018. Communications in Computer and Information Science, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-00350-0_15
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