Towards Improved Recognition and Diagnosis of Autism in Females

Overview

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and verbal impairments, affecting 1 in 36 children in the United States, with males being diagnosed 4 times as often as females. ASD diagnosis is based on behavioral observations, caregiver interviews, and clinical questionnaires. Autism in females, compared to males, is more likely to be missed, as their behavioral symptoms present in ways that may not fit current diagnostic criteria. This study aims to evaluate the sex differences in current diagnostic tests for autism and identify behaviors that can predict autism more accurately among females. A public dataset (the Autism Brain Imaging Data Exchange) of 1112 individuals: 948 males, 164 females, 539 autistic, and 573 non-autistic was used. This included test scores for the common diagnostic instruments: Autism Diagnostic Observation Schedule (ADOS), Autism Diagnostic Interview-Revised (ADI-R), Social Communication Questionnaire (SCQ), and Social Responsiveness Scale (SRS). Exploratory data analysis revealed statistically significant gender differences for SCQ total, SRS total, and SRS communication scores. Additional analysis via Machine Learning (ML) further supported these sex differences. Autism classification accuracy with current diagnostic behaviors was 95% for males compared to 85% for females. A minimal set of 12 most predictive behavioral features for females was derived from the original 23 (reduction of 48%) and run on six ML algorithms. Random Forest had the highest accuracy (91%) in classifying female autism with this reduced feature set. These results support potential modifications to autism diagnostic criteria for females, reducing overreliance on ADOS and emphasizing questionnaires like SCQ and SRS and interviews (ADI-R) more. Building automated screening tools aligned to these sex-specific behaviors can facilitate faster, more accurate, and more equitable autism diagnoses.

Competition history

• AJAS 2025 Category not listed

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