Autism detection in High-Functioning Adults with the application of Eye-Tracking technology and Machine Learning

Abstract

High-Functioning Autism Detection in Adults is significantly difficult compared with early Autism Spectrum Disorder (ASD) diagnosis with severe symptoms. ASD diagnosis is usually achieved by behavioural instruments relying on subjective rather on objective criteria, whereas advances in research indicate cutting -edge methods for early assessment, such as eye-tracking technology, machine learning, Internet of Things (IoT), and other assessment tools. This study suggests the detection of ASD in high-functioning adults with the contribution of Transfer Learning. Decision Trees, Logistic Regression and Transfer Learning were applied on a dataset consisting of high-functioning ASD adults and controls, who looked for information within web pages. A high classification accuracy was achieved regarding a Browse (80.50%) and a Search (81%) task showing that our method could be considered a promising tool regarding automatic ASD detection. Limitations and suggestions for future research are also included.