ERIC Number: EJ1442788
Record Type: Journal
Publication Date: 2024-Nov
Pages: 16
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0162-3257
EISSN: EISSN-1573-3432
Machine Learning Differentiation of Autism Spectrum Sub-Classifications
R. Thapa; A. Garikipati; M. Ciobanu; N.P. Singh; E. Browning; J. DeCurzio; G. Barnes; F.A. Dinenno; Q. Mao; R. Das
Journal of Autism and Developmental Disorders, v54 n11 p4216-4231 2024
Purpose: Disorders on the autism spectrum have characteristics that can manifest as difficulties with communication, executive functioning, daily living, and more. These challenges can be mitigated with early identification. However, diagnostic criteria has changed from DSM-IV to DSM-5, which can make diagnosing a disorder on the autism spectrum complex. We evaluated machine learning to classify individuals as having one of three disorders of the autism spectrum under DSM-IV, or as non-spectrum. Methods: We employed machine learning to analyze retrospective data from 38,560 individuals. Inputs encompassed clinical, demographic, and assessment data. Results: The algorithm achieved AUROCs ranging from 0.863 to 0.980. The model correctly classified 80.5% individuals; 12.6% of individuals from this dataset were misclassified with another disorder on the autism spectrum. Conclusion: Machine learning can classify individuals as having a disorder on the autism spectrum or as non-spectrum using minimal data inputs.
Descriptors: Autism Spectrum Disorders, Symptoms (Individual Disorders), Clinical Diagnosis, Artificial Intelligence, Classification
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Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A