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Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis
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  • Published: 19 February 2026

Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis

  • Erik Van der Burg  ORCID: orcid.org/0000-0003-2522-79251 na1,
  • Robert M. Jertberg  ORCID: orcid.org/0000-0002-4077-90771 na1,
  • Hilde M. Geurts2,3,
  • Bhismadev Chakrabarti4,5,6 &
  • …
  • Sander Begeer1 

Translational Psychiatry , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Diagnostic markers
  • Human behaviour

Abstract

Traditional subjective measures are limited in the insight they provide into underlying behavioral differences associated with autism and, accordingly, their ability to predict diagnosis. Performance-based measures offer an attractive alternative, being designed to capture neuropsychological constructs more directly and objectively. However, due to the heterogeneity of autism, differences in any one specific neuropsychological domain are inconsistently detected. Meanwhile, protracted wait times for diagnostic interviews delay access to care, highlighting the importance of developing better methods for identifying individuals likely to be autistic and understanding the associated behavioral differences. We administered a battery of online tasks measuring multisensory perception, emotion recognition, and executive function to a large group of autistic and non-autistic adults. We then used machine learning to classify participants and reveal which factors from the resulting dataset were most predictive of diagnosis. Not only were these measures able to predict autism in a late-diagnosed population known to be particularly difficult to identify, their combination with the most popular screening questionnaire enhanced its predictive accuracy (reaching 92% together). This indicates that performance-based measures may be a promising means of predicting autism, providing complementary information to existing screening questionnaires. Many variables in which significant group differences were not detected had predictive value in combination, suggesting complex latent relationships associated with autism. Machine learning’s ability to harness these connections and pinpoint the most crucial features for prediction could allow optimization of a screening tool that offers a unique marriage of predictive accuracy and accessibility.

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Data availability

The machine learning python script can be found at https://osf.io/nxyzt/?view_only=b9b60eeaf0f54495aa81fb2fc4955d97. The data from the Netherlands Autism Register (NAR) are not publicly available due to privacy restrictions. Researchers can request access by submitting a research proposal and signing a Data Sharing Agreement with the NAR team. More information is available on the NAR website (https://nar.vu.nl/nl).

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Acknowledgements

Special thanks to the volunteer participants at the Netherlands Autism Register. Funding provided by ZonMw (60-63600-98-834), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWA.1518.22.136 SCANNER), the Medical Research Council UK (Grant ref: MR/S036423/1), and the European Research Council (Grant ref: 865568).

Author information

Author notes

  1. These authors contributed equally: Erik Van der Burg, Robert M. Jertberg.

Authors and Affiliations

  1. Section Clinical Developmental Psychology, Vrije Universiteit Amsterdam and the Netherlands and Amsterdam Public Health Research Institute, Amsterdam, The Netherlands

    Erik Van der Burg, Robert M. Jertberg & Sander Begeer

  2. Dutch Autism and ADHD research Center (d’Arc), Brain & Cognition, Department of Psychology, Universiteit van Amsterdam, Amsterdam, The Netherlands

    Hilde M. Geurts

  3. Leo Kannerhuis (Youz/Parnassiagroup), Amsterdam, The Netherlands

    Hilde M. Geurts

  4. Centre for Autism, School of Psychology and Clinical Language Sciences, University of Reading, Reading, UK

    Bhismadev Chakrabarti

  5. India Autism Center, Kolkata, India

    Bhismadev Chakrabarti

  6. Department of Psychology, Ashoka University, Haryana, India

    Bhismadev Chakrabarti

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Conceptualization: EVdB, RMJ, HMG, BC, SB; Methodology: EVdB, RMJ, HMG, BC, SB; Software: EVdB, RMJ; Formal Analysis: EVdB, RMJ; Writing – Original draft: EVdB, RMJ; Writing – Review & Editing: EVdB, RMJ, HMG, BC, SB; Visualization: EVdB; Funding Acquasition: EVdB, HMG, BC, SB.

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Correspondence to Robert M. Jertberg or Bhismadev Chakrabarti.

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Van der Burg, E., Jertberg, R.M., Geurts, H.M. et al. Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis. Transl Psychiatry (2026). https://doi.org/10.1038/s41398-026-03823-y

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  • Received: 02 May 2025

  • Revised: 11 December 2025

  • Accepted: 20 January 2026

  • Published: 19 February 2026

  • DOI: https://doi.org/10.1038/s41398-026-03823-y

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