Autism spectrum disorder (ASD) is a brain development disorder that restricts a person's communication abilities and social interaction capabilities from natural growth. In this paper, we have applied various supervised classification techniques to detect the presence of child autism. Our findings show that the Sequential Minimal Optimization (SMO) classifier performs best to detect ASD cases with the highest accuracy and minimum execution time and error rate. We also identify the most dominant features in dectecting child autism.
Keywords: Autism Spectrum Disorder; Child; Supervised Machine Learning.