Investigating Multiclass Autism Spectrum Disorder Classification using Machine Learning Techniques

The diagnosis and classification of autism spectrum disorder (ASD) presents anatomical difficulty owing to the existence of a wide range of symptoms that may be organized into many categories. The present research investigates the efficacy of machine learning methods for facilitating the recognition of individuals who have been diagnosed with ASD. The primary aim of this study has been to assess the effectiveness of multiple algorithms based on machine learning in identifying intricate patterns seen in datasets related to ASD, which includes a wide range of diagnostic categories. The results indicate that the Logistic Regression approach demonstrated great levels of accuracy, with rates of 94.3% for children and 99% for adolescents in the binary classification system. Similarly, it has been reported that the Support Vector Machine (SVM) had superior performance compared to all other systems in the binary classification test focused on adults exclusively, with an accuracy rate of 98.5%. Moreover, a supplementary series of experiments conducted on the combined dataset of children, adolescents, and adults has resulted in the observation that Logistic Regression and SVM exhibited notable accuracy rates of 97.2% for binary classification and 99.55% for multiclass classification, encompassing individuals from diverse age groups. The results provide evidence in favor of that progress has been achieved in the diagnosis and treatment of ASD as a result of the capacity to detect and categorize the disorder at an earlier developmental phase.