An exploration of machine learning approaches for early Autism Spectrum Disorder detection

Abstract

Autism Spectrum Disorder is a neurodevelopmental condition impacting an individual’s repetitive behaviours, social skills, verbal and nonverbal communication abilities, and capacity for acquiring new knowledge. Manifesting typically in early childhood, specifically between 6 months and 5 years, the symptoms of autism exhibit a progressive nature over time. This study explores the application of Logistic Regression, Support Vector Classifier, K-Nearest Neighbour, Decision Tree, and Random Forest for predicting Autism in children and toddlers by leveraging advancements in machine learning. The efficacy of these techniques is evaluated using publicly accessible datasets specific to both age groups. The findings indicate remarkable performance, with the toddler dataset achieving a mean Intersection over Union (mIoU) of 100$\text{\%}$ for Support Vector Classifier and 99.80$\text{\%}$ for Logistic Regression. Similarly, the children dataset demonstrates outstanding results, achieving an mIoU of 100$\text{\%}$ for Support Vector Classifier and 99.96$\text{\%}$ for Logistic Regression. Furthermore, all algorithms achieved 100$\text{\%}$ accuracy on the children (age 4–11) dataset collected from real-world sources. Logistic Regression, Random Forest, Support Vector Classifier, and Decision Tree attained 100$\text{\%}$ accuracy and mIoU with the real-world dataset. These results underscore the potential of machine learning in aiding the early detection of ASD in children and toddlers, offering promising avenues for future research and clinical applications.