Comparative Exploration of Machine Learning Models for Enhanced Autism Detection

Autism Spectrum Disorder (ASD) presents a complex neurodevelopmental challenge impacting communication, behavior, and social interactions. Detecting and intervening early is pivotal, especially as ASD prevalence rises, necessitating effective and cost-efficient screening methods. This study delves into the potential of artificial intelligence (AI) and machine learning (ML) technologies, exploring algorithms like XGBClassifier, Random Forest, Decision Trees, AdaBoostClassifier, KMeans, and Artificial Neural Networks for early ASD prediction. Through an extensive literature survey and analysis of detection techniques, the research aims to contribute to reliable screening tool development. Each algorithm's strengths and weaknesses are discussed, highlighting the significance of accuracy, efficiency, and interpretability in model selection. The paper emphasizes the transformative potential of these technologies in ASD detection, promising more precise, economically viable, and timely interventions for improved outcomes. Ongoing efforts to refine and validate algorithms with diverse datasets are stressed to ensure real-world reliability and effectiveness.