Forecasting Autism Spectrum Disorder using Machine Learning in Cloud Computing

Abstract

Autism Spectrum Disorder (ASD) poses significant challenges in early diagnosis and intervention due to its heterogeneous presentation. Machine learning (ML) techniques offer promising avenues for improving ASD prediction accuracy and efficiency. This paper provides a comprehensive review of ML methodologies for ASD prediction, including feature selection, model architectures, performance evaluation, and challenges. We analyze the strengths and limitations of existing approaches and propose future research directions. By synthesizing current literature, we aim to contribute to the advancement of ASD prediction using ML, facilitating early detection and intervention strategies.