Developing an AI-Driven Mobile Application for Early Autism Diagnosis and Classification

Abstract

This dissertation looks at the important issue of delaying diagnosis and treatment in autism spectrum disorder (ASD) by creating a mobile app that uses AI to help with early diagnosis and classification of the disorder. It collects and reviews large datasets that include behavioral evaluations, developmental milestones, and known diagnostic standards, training an AI model able to make correct predictions about ASD. The main results show that the app not only speeds up diagnosis but also improves classification accuracy compared to standard diagnostic methods. These findings highlight the importance of using advanced technology in healthcare, especially for ASD, where early intervention is key to positive developmental results. Additionally, this study points out how AI tools can help fill gaps in healthcare delivery, especially in areas with fewer resources and access to specialized care. The implications go beyond just autism diagnosis, indicating a significant potential for AI use in different healthcare fields, aiming to enhance diagnostic processes and customize treatment plans, thus ultimately boosting patient outcomes and supporting public health efforts.