Machine Learning Prediction of Autism Spectrum Disorder From a Minimal Set of Medical and Background Information
In this diagnostic study of ML prediction of ASD, robust model performance was observed to identify autistic individuals with more symptoms and lower cognitive levels. The robustness and ML model generalizability results are promising for further validation and use in clinical and population settings.
Authors
- Ashraf Yahia (ORCID: https://orcid.org/0000-0002-0376-8264)
- Shyam Sundar Rajagopalan (ORCID: https://orcid.org/0009-0005-5461-7465)
- Kristiina Tammimies (ORCID: https://orcid.org/0000-0002-8324-4697)
- Yali Zhang (ORCID: https://orcid.org/0009-0008-6152-2562)
Institutions
- Karolinska University Hospital (SE)
- Karolinska Institutet (SE)
- Institute of Bioinformatics and Applied Biotechnology (IN)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-08-19
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.29229
- Citations
- 47
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 16.11
Funders
- Stiftelsen för Strategisk Forskning
- Vetenskapsrådet
- China Scholarship Council
- Forskningsrådet om Hälsa, Arbetsliv och Välfärd
- Uppsala Multidisciplinary Center for Advanced Computational Science