Abstract
Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today's pediatric population, the methods of diagnosis remain subjective, cumbersome and time intensive. With gaps upward of a year between initial suspicion and diagnosis, valuable time where treatments and behavioral interventions could be applied is lost as these disorders remain undetected. Methods to quickly and accurately assess risk for these, and other, developmental disorders are necessary to streamline the process of diagnosis and provide families access to much-needed therapies sooner. Using forward feature selection, as well as undersampling and 10-fold cross-validation, we trained and tested six machine learning models on complete 65-item Social Responsiveness Scale score sheets from 2925 individuals with either ASD (n=2775) or ADHD (n=150). We found that five of the 65 behaviors measured by this screening tool were sufficient to distinguish ASD from ADHD with high accuracy (area under the curve=0.965). These results support the hypotheses that (1) machine learning can be used to discern between autism and ADHD with high accuracy and (2) this distinction can be made using a small number of commonly measured behaviors. Our findings show promise for use as an electronically administered, caregiver-directed resource for preliminary risk evaluation and/or pre-clinical screening and triage that could help to speed the diagnosis of these disorders.
Highlights
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are among the most common childhood disorders, with the most recent prevalence estimates by the Centers for Disease Control at 1.5% and 9.5%, respectively.[1,2] ASD and ADHD have considerable behavioral overlaps, including impulsivity and trouble with social interactions.[3]
A child with predominant ADHD might struggle with social interactions, but this may stem from inattention to the speaker or interrupting due to impulsivity, rather than a fundamental misunderstanding of social cues, which more strongly aligns to a child with autism
For each trial in the 10-fold cross-validation, mutual information feature ranking was performed on the nine folds designated as the training set
Summary
Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are among the most common childhood disorders, with the most recent prevalence estimates by the Centers for Disease Control at 1.5% and 9.5%, respectively.[1,2] ASD and ADHD have considerable behavioral overlaps, including impulsivity and trouble with social interactions.[3]. The most recent Diagnostic and Statistical Manual (DSM-V)[4] has recognized the frequency of co-occurrence of ASD and ADHD symptoms and has altered its diagnostic criteria, which previously precluded a dual-diagnosis under DSM-IV, to clinically formalize the autism-ADHD comorbidity Both ASD and ADHD are identified through extensive examination, including evaluation by a team of behavioral pediatricians and child psychologists as well as administration of a number of diagnostic assessments by certified professionals. These rigorous diagnostic examinations often last multiple hours, and the everincreasing demand for these appointments far exceeds the maximum capacity for developmental pediatrics clinics across the country. The resulting classifiers show promise for use as pre-clinical screening tools for the evaluation of ASD/ADHD risk
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