Utilizing Fuzzy Logic for Autism Detection: An Expert System Framework

Autism, a complex neurological disorder, presents lifelong challenges that often emerge during early childhood, typically between the ages of 12 to 18 months. Accurate assessment of autism is essential, but conventional diagnostic approaches are hindered by inherent issues of vagueness, imprecision, randomness, ignorance, and incompleteness. In this context, our primary goal is to develop a fuzzy rule-based expert system to enhance the diagnosis of various autism types, including Attention-Deficit/Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Down Syndrome, Cerebral Palsy, and Intellectual Disability. Utilizing MATLAB software, we have integrated a wide array of indicators and symptoms drawn from inputs provided by parents of autistic children and domain experts. This expert system streamlines the diagnostic process for healthcare professionals, leveraging fuzzy rule-based expertise to uncover significant signs and symptoms associated with autism. Importantly, our approach effectively accommodates uncertainties that often challenge conventional diagnostic methods.