A Review of Machine Learning Models to Detect Autism Spectrum Disorders (ASD)

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that can manifest in a variety of ways. One common characteristic is difficulty with communication, which may manifest as difficulty understanding others or expressing oneself effectively. Social interaction can also be challenging, as individuals with ASD may struggle to comprehend social cues or adapt to new situations. Many machine-learning models have been developed or are in progress to detect ASD automatically. Three machine learning model-based frameworks have been studied and elaborated on, each with a clear concept of the detection of ASD among children and adults. This research paper has done a closer review of these frameworks and their datasets to diagnose ASD automatically. In the first framework, deep learning models such as Xception, VGG19, and NASNetMobile have been utilized for the detection of autism spectrum disorder (ASD). In addition, other models such as XGBoost, Neural Network, and Random Forest have been employed in the second framework to detect ASD from a clinical standard screening dataset for toddlers. Meanwhile, the third framework involves traditional machine learning models that have been trained using the UCI dataset for ASD. The accuracy of each model has been discussed and elaborated on.