The Importance of Data Sources for Machine Learning Applications in Autism: A Mini Review

Abstract

Autism spectrum disorder (ASD) is a group of lifelong heterogeneous neurodevelopmental conditions with a wide range of severity levels that affect social communication and social interaction. Diagnosis of ASD relies on subjective observation of these clinical phenotypes. The growing body of big data generated by subjective methods and more recently by objective high-throughput technologies such as omics for the detection of biomolecules, is being successfully applied to a rapidly-growing number of machine learning (ML) algorithms to inform research for diagnostics and interventions for patients with ASD. While most reviews in this area are focused on the ML approaches, we highlight the impact of the database on the expected outcomes in ML-based ASD research studies.