Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning

The study of appropriate and accurate classification for “autism spectrum disorder (ASD)” is crucial, and this study, “Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning,” aims to fulfil this requirement. The study integrates both “quantitative and qualitative methodologies” through an integrated approach and accessible study philosophy. Approaches for gathering data include compiling datasets, reviewing relevant research, and obtaining EEG, emotions, and eye motion data. In order to boost the accuracy of ASD screening, statistical models including “logistic regression, neural networks, and support vector machines have been created.” This quantitative analysis is enhanced by a thematic approach, which pinpoints recurrent themes and characteristics. Data protection and permission from subjects are given top priority in this study's ethical concerns. The theoretical and practical divide, the studies hope to improve effective ASD diagnosis and treatments.