A Hybrid Model Based on Fuzzy Rules to Act on the Diagnosed of Autism in Adults

Aspects of Autistic Spectrum Disorder (ASD) can be diagnosed, with rare frequency, in people already in adulthood. To aid in the diagnosed of autistic traits, a mobile system was developed with the objective of executing the techniques extracted from expert studies to determine the effective diagnosis of the disease. This type of system uses artificial intelligence capabilities and machine learning techniques to assign probabilities to people who pass the in-app test. According to the information provided by the authors of the mobile application, future research could address the use of other intelligent models to assist in predicting whether or not the patient has traits of autism. Therefore, this paper proposes the insertion of a hybrid interpretive technique based on the synergy of the concepts of artificial neural networks and fuzzy systems trained by the extreme learning machine to generate fuzzy rules to deal with questions provided by users seeking to obtain immediate answers on preliminary diagnoses of autism in adults. The tests performed achieved high levels of accuracy superior to the preliminary studies that inspired this research, making it a viable alternative for the efficient diagnosed of autism in adults.