Detection of Autism Disorder using Machine Learning Techniques

The complex neurodevelopmental disorder known as autism spectrum disorder (ASD) is defined by limited, repetitive patterns of behavior, interests, or activities, as well as ongoing difficulties with social communication and engagement. Acknowledging the vital importance of proactive intervention, this project aims to address this critical concern. A multi-dataset approach is adopted, incorporating four different datasets for different age groups (Children, Adolescent, Young, Adults) to improve diagnostic accuracy. By leveraging Flask, an intuitive user interface is created where participants could easily access and complete the comprehensive questionnaire aimed at capturing ASD-related symptoms and behaviors. Monitoring, screening, and assessing a person’s development is crucial to diagnosing ASD as early as possible. This allows us to provide the right care and support for an autistic person, enabling them to realize their full potential. By integrating data from various age groups, ranging from childhood to adulthood, the project aims to develop a comprehensive understanding of ASD across the lifespan.