Random Forest Machine Learning Matches Human Expert Accuracy in Trauma Severity Scoring
Random forest ML algorithms accurately predict missing AIS and ISS scores, significantly improving trauma registry data completeness while maintaining clinical accuracy equivalent to human expert scoring.
Authors
- Howard Wain (ORCID: https://orcid.org/0000-0002-6693-0062)
- John Bruce (ORCID: https://orcid.org/0000-0001-8666-4104)
- Grant Laing (ORCID: https://orcid.org/0000-0001-8075-0386)
- Wanda Bekker (ORCID: https://orcid.org/0000-0003-0695-5994)
- Damian Clarke (ORCID: https://orcid.org/0000-0002-8467-1455)
- V Manchev
Institutions
- University of the Witwatersrand (ZA)
- University of KwaZulu-Natal (ZA)
Publication Details
- Journal
- World Journal of Surgery
- Published
- 2025-09-24
- DOI
- https://doi.org/10.1002/wjs.70114
- Citations
- 2
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 4.51