A SURVEY ON AUTISM DETECTION USING MACHINE LEARNING

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social interaction, communication, and repetitive behaviours. Early diagnosis and intervention significantly impact the long-term outcomes for individuals with ASD. However, the complexity and heterogeneity of ASD pose challenges to accurate and timely detection. The existing literature on autism detection through ML showcases promising developments. Various studies have explored the application of ML algorithms to diverse datasets, ranging from behavioral observations to neuro imaging data. These studies have demonstrated the potential of ML in providing objective and data-driven insights into the diagnostic process. However, there remains a need for further research to refine and expand these approaches. This research builds upon the foundation laid by previous studies, aiming to address gaps in the literature. By employing advanced ML techniques, we seek to enhance the accuracy of autism detection models in early stage and contribute to the ongoing efforts to develop efficient, scalable, and reliable diagnostic tools. Keywords: Autism Spectrum Disorder, ASD, machine learning (ML), brain development disorder, Behavioral Analytics