An intelligent Bayesian hybrid approach to help autism diagnosis.

This paper proposes a Bayesian hybrid approach based on neural networks and fuzzy systems to construct fuzzy rules to assist experts in detecting features and relations regarding the presence of autism in human beings. The model proposed in this paper works with a database generated through mobile devices that deals with diagnoses of autistic characteristics in human beings who answer a series of questions in a mobile application. The Bayesian model works with the construction of Gaussian fuzzy neurons in the first and logical neurons in the second layer of the model to form a fuzzy inference system connected to an artificial neural network that activates a robust output neuron. The new fuzzy neural network model was compared with traditional state-of-the-art machine learning models based on high-dimensional based on real-world data sets comprising the autism occurrence in children, adults, and adolescents. The results (97.73- Children/94.32-Adolescent/97.28-Adult) demonstrate the efficiency of our new method in determining children, adolescents, and adults with autistic traits (being among the top performers among all ML models tested), can generate knowledge about the dataset through fuzzy rules.