Enhanced Early Identification of Autism Spectrum Disorder using Deep Learning and Advanced Machine Learning

A neurological condition affecting individuals throughout their lives, Autism Spectrum Disorder (ASD) profoundly impacts behavior, activities, cognition, and socio-communication abilities. Timely detection in children between ages two and three is crucial for early intervention. This study employs standard machine learning techniques, and deep learning techniques to predict ASD. Models are rigorously validated using accuracy, precision, and recall measurements. To provide a centralized framework for researchers, comparative analyses consider application types, simulation techniques, comparison approaches, and input data. Additionally, the study includes the best model prediction, highlighting the model that performed most accurately in predicting ASD. Hyperparameter calculation is integral to the study, with optimized hyperparameters determined through meticulous tuning, enhancing the models’ predictive capabilities and overall performance. The goal of the entire approach is to offer a solid foundation for early ASD diagnosis and intervention.