Abstract
Intelligent decision tree-based software was built to aid in predicting the adolescent with autism traits. This application, which is obtained and operated on mobile devices, uses artificial intelligence and machine learning techniques to assign probabilities to people who pass the test in the application. The app has a knowledge base that assists in the prediction of autism in adolescents. This paper intends to demonstrate the feasibility of using fuzzy neural networks to assist in predicting the identification of autism traits, mainly supported by a system capable of generating fuzzy rules more cohesive than a decision tree. Therefore, this article proposes the insertion of an interpretive technique based on an extreme learning machine to deal with questions provided by users that seek to obtain more immediate answers, based on classification binary labels. The tests performed with the base achieved high levels of precision for the proposed model and support, making it a viable alternative for the efficient prediction of adolescents with autism.
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The thanks of this work are destined to CEFET-MG and UNA.
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de Campos Souza, P.V., Guimaraes, A.J., Araujo, V.S., Rezende, T.S., Araujo, V.J.S. (2019). Using Fuzzy Neural Networks Regularized to Support Software for Predicting Autism in Adolescents on Mobile Devices. In: Elhoseny, M., Singh, A. (eds) Smart Network Inspired Paradigm and Approaches in IoT Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-8614-5_7
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