Machine Learning and Deep Learning Techniques for the Prediction of Autism Spectrum Disorder: A Comprehensive Review

Abstract

Early identification of Autism Spectrum Disorder (ASD) is crucial for enabling timely intervention and improving developmental outcomes. Conventional diagnostic methods rely heavily on behavioral observation and expert judgment, often leading to delayed diagnosis. In recent years, machine learning and deep learning techniques have been increasingly explored to support early ASD prediction using facial images, behavioral videos, neuro imaging data, and clinical datasets. This review presents a comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive performance, and key limitations. The findings indicate that deep learning-based approaches, particularly those using facial images and behavioral video analysis, generally achieve higher accuracy compared to traditional machine learning models. However, challenges such as limited dataset diversity, reduced cross-dataset generalization, lack of interpretability, and insufficient clinical validation remain prevalent across studies. In addition to the review, this work includes the implementation of an existing deep learning-based facial image classification method to validate reported findings. Overall, this critical review highlights current progress and emphasizes the need for robust, interpretable, and clinically validated approaches to support early ASD screening and complement traditional diagnostic practices.