Enhancing Early Autism Prediction Using Machine Learning Techniques

Autism is a complex neuro-developmental disorder. Difficulties in communication, social engagement, and repetitive behavior are hallmarks of ASD. Long-term results for people with ASD have been markedly improved by early diagnosis and care. To enhance early intervention methods, this research aims to examine the use of machine learning techniques to predict autism spectrum disorder (ASD) in the early development stage. The main objective of this study is to summarize the condition of the field's research and identify its key trends, obstacles, and prospects. A comprehensive review of the literature on recent advancements in the field of predicting autism spectrum disorder (ASD) with machine learning approaches to enhance early intervention strategies was done on PubMed, Google Scholar, and Scopus. The series of research studies that have investigated the use of machine learning algorithms in predicting ASD are methodically examined in this paper. The review's findings illustrate how machine learning could improve early intervention initiatives for ASD. By providing a comprehensive overview and critical evaluation of previous research, this study advances our knowledge of methods that have demonstrated encouraging outcomes in predicting autism.