Machine Learning-Based Feature Selection for Predicting Autism Spectrum Disorder in Pediatric Neurodevelopmental Disorders

In this study, we aimed to utilize machine learning techniques to facilitate the early detection of autism in children, with the understanding that early intervention can significantly improve treatment outcomes and enhance self-sufficiency in adult life. Our research focused on 125 samples obtained from clinics in Yazd, Iran. We extracted a total of 22 features, comprising both relevant and irrelevant factors, to train classifiers capable of distinguishing between autistic and non-autistic patients. Employing K-nearest neighbor, support vector machine, and random forest algorithms, we achieved high classification scores, indicating the efficacy of machine learning in autism detection. Furthermore, our analysis identified several features exhibiting significant correlations with autism, providing potential markers for reliable initial diagnosis. By shedding light on these predictive features, our study aims to address the challenges faced by clinics and parents in identifying autism spectrum disorder, ultimately improving diagnostic accuracy and early intervention strategies.