Clinical Correlates of Errors in Machine-Learning Diagnostic Model of Autism Spectrum Disorder: Impact of Sample Cohorts
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| Title: | Clinical Correlates of Errors in Machine-Learning Diagnostic Model of Autism Spectrum Disorder: Impact of Sample Cohorts |
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| Language: | English |
| Authors: | Yen-Chin Wang (ORCID |
| Source: | Autism: The International Journal of Research and Practice. 2025 29(12):3083-3099. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Artificial Intelligence, Autism Spectrum Disorders, Clinical Diagnosis, Error Patterns, Models, Classification, Sex, Age, Intelligence Quotient, Symptoms (Individual Disorders), Mental Disorders, Behavior Problems, Attention Deficit Hyperactivity Disorder, Aggression, Attention, Foreign Countries, Diagnostic Tests |
| Geographic Terms: | Taiwan |
| Assessment and Survey Identifiers: | Social Responsiveness Scale, Child Behavior Checklist, Autism Diagnostic Observation Schedule |
| DOI: | 10.1177/13623613251360271 |
| ISSN: | 1362-3613 1461-7005 |
| Abstract: | Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan: the clinical cohort comprised 1203 autistic participants and 1182 non-autistic comparisons, and the community cohort consisted of 35 autistic participants and 3297 non-autistic comparisons. Classification models were trained, and the misclassification cases were investigated regarding their associations with sex, age, intelligence quotient (IQ), symptoms from the child behavioral checklist (CBCL), and co-occurring psychiatric diagnosis. Models showed high within-cohort accuracy (clinical: sensitivity 0.91-0.95, specificity 0.93-0.94; community: sensitivity 0.91-1.00, specificity 0.89-0.96), but generalizability across cohorts was limited. When the community-trained model was applied to the clinical cohort, performance declined (sensitivity 0.65, specificity 0.95). In both models, non-autistic individuals misclassified as autistic showed elevated behavioral symptoms and attention-deficit hyperactivity disorder (ADHD) prevalence. Conversely, autistic individuals who were misclassified tended to show fewer behavioral symptoms and, in the community model, higher IQ and aggressive behavior but less social and attention problems. Error patterns of machine-learning model and the impact of training data warrant careful consideration in future research. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1489398 |
| Database: | ERIC |
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| Abstract: | Machine-learning models can assist in diagnosing autism but have biases. We examines the correlates of misclassifications and how training data affect model generalizability. The Social Responsive Scale data were collected from two cohorts in Taiwan: the clinical cohort comprised 1203 autistic participants and 1182 non-autistic comparisons, and the community cohort consisted of 35 autistic participants and 3297 non-autistic comparisons. Classification models were trained, and the misclassification cases were investigated regarding their associations with sex, age, intelligence quotient (IQ), symptoms from the child behavioral checklist (CBCL), and co-occurring psychiatric diagnosis. Models showed high within-cohort accuracy (clinical: sensitivity 0.91-0.95, specificity 0.93-0.94; community: sensitivity 0.91-1.00, specificity 0.89-0.96), but generalizability across cohorts was limited. When the community-trained model was applied to the clinical cohort, performance declined (sensitivity 0.65, specificity 0.95). In both models, non-autistic individuals misclassified as autistic showed elevated behavioral symptoms and attention-deficit hyperactivity disorder (ADHD) prevalence. Conversely, autistic individuals who were misclassified tended to show fewer behavioral symptoms and, in the community model, higher IQ and aggressive behavior but less social and attention problems. Error patterns of machine-learning model and the impact of training data warrant careful consideration in future research. |
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| ISSN: | 1362-3613 1461-7005 |
| DOI: | 10.1177/13623613251360271 |
