Supervised Machine Learning for a Novel Autism Prediction Tool in Adults

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Brandsen, S., Hallur, S., Andrews, I., Naqvi, M., Zhou, S., Boheler, J., & Chandrasekhar, T. (2024). Supervised Machine Learning for a Novel Autism Prediction Tool in Adults [Preprint]. Center for Open Science. https://doi.org/10.31219/osf.io/89r3u

Publication date: 13 Dec 2024 Added to AutiHub: 5 Jul 2026 Type: Preprint Article language: English
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Authors

Publication authors
7
Publication authors identified as autistic
1 / 7 (14.3%)
  1. Sam Brandsen *
  2. Shreyas Hallur
  3. Isabelle Andrews
  4. Madeeha Naqvi
  1. Shiru Zhou
  2. Jacklyn Boheler
  3. Tara Chandrasekhar

Abstract

For many late-identified autistic adults, the realization that they are autistic can be a key first step to accessing supportive resources and accommodations and developing self-understanding. We introduce a novel screening tool for autistic traits designed primarily by autistic adults. It includes options to assess masking, sensory processing differences, commonly co-occurring medical or mental health conditions, and questions about social and communication differences and repetitive behavior. We used a simple supervised machine learning algorithm to generate a score predicting whether the individual is autistic. Our results indicate that this algorithm was able to distinguish respondents who are non-autistic from respondents who self-reported being formally diagnosed as autistic. Additionally, our algorithm remained effective in identifying autism in respondents with other under-represented identities. Finally, we separately analyzed the responses of self-identified autistic individuals and found a high degree of overlap in the responses of self-identified and formally diagnosed autistic respondents.

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These indicators describe the cited bibliography imported for this publication. Cited-reference metrics use the cited-reference total as denominator. Cited-author metrics state whether they use all cited-author occurrences or only occurrences linked to authors already integrated in the AutiHub database. They use cached links between cited authors and authors integrated in the AutiHub database. Last computed: 16 Aug 2026 11:30.

Cited references
22
Total cited references integrated for this publication.
Cited references with an identified autistic author
2 / 22 (9.1%)
Cited author occurrences identified as autistic
3 / 108 (2.8%)
Among occurrences linked to AutiHub author records: 3 / 16 (18.8%). Distinct cited authors identified as autistic: 3 / 106 (2.8%).
Cited author occurrences linked to AutiHub author records
16 / 108 (14.8%)
Distinct linked cited authors: 16 / 106 (15.1%)
Linked cited-author occurrences not identified as autistic
13 / 16 (81.2%)
Among linked cited-author occurrences only. Across all cited-author occurrences: 13 / 108 (12.0%). Distinct linked cited authors not identified as autistic: 13 / 16 (81.2%).
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