Interpretable Fuzzy System for Early Detection Autism Spectrum Disorder

Abstract

Autism spectrum disorder (ASD) is a chronic developmental impairment that impairs a person's ability to communicate and connect with others. In people with ASD, social contact and reciprocal communication are continually jeopardized. People with ASD may require varying degrees of psychological aid in order to gain greater independence, or they may require ongoing supervision and care. Early discovery of ASD results in more time allocated to individual rehabilitation. In this study, we proposed the fuzzy classifier for ASD classification and tested its interpretability with the fuzzy index and Nauck's index to ensure its reliability. Then, the rule base is created with the Gauje tool. The fuzzy rules were then applied to the fuzzy neural network to predict autism. The suggested model is built on the Mamdani rule set and optimized using the backpropagation algorithm. The proposed model uses a heuristic function and pattern evolution to classify dataset. The model is evaluated using the benchmark metrics accuracy and F-measure, and Nauck's index and fuzzy index are employed to quantify interpretability. The proposed model is superior in its ability to accurately detect ASD, with an average accuracy rate of 91% compared to other classifiers.