Analyzing Autism Spectrum Disorder in Children

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that presents diagnostic challenges.This study examines a dataset that includes demographic, medical, and familial variables, in addition to responses to the 10-item Autism Spectrum Questionnaire for Children aged 4-11 years old (AQ-10-Child) to assess their potential in improving ASD detection.Both responses from parents of these children and self-reporting are integrated.The dataset is available at UCI Machine Learning Repository.Analyzing 292 participants, this research found no significant correlations between family history of ASD, jaundice, gender, and ethnicity, and ASD, likely due to the small sample size.These findings highlight the need for larger, more diverse datasets to improve diagnostic accuracy of ASD.Future research should focus on integrating machine learning and AIdriven tools to enhance early ASD detection and intervention strategies.