Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm.
Our study highlights the strong potential of machine learning models, especially Random Forest and Gradient Boosting, in predicting chest trauma outcomes with high accuracy and sensitivity.
Authors
- Arash Sarveazad (ORCID: https://orcid.org/0000-0001-9273-1940)
- Mohsen Masoodi (ORCID: https://orcid.org/0000-0001-8471-9220)
- Hamed Zarei (ORCID: https://orcid.org/0000-0002-2170-079X)
- Mohammadhossein Vazirizadeh-Mahabadi (ORCID: https://orcid.org/0000-0003-2435-9985)
- Mostafa Hosseini (ORCID: https://orcid.org/0000-0002-1334-246X)
- Mobina Yarahmadi (ORCID: https://orcid.org/0000-0003-2543-8597)
- Amir Ghaffari Jolfayi (ORCID: https://orcid.org/0000-0002-9223-5565)
- Mahmoud Yousefifard (ORCID: https://orcid.org/0000-0001-5181-4985)
Institutions
- Iran University of Medical Sciences (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- PubMed
- Published
- 2025-01-01
- DOI
- https://doi.org/10.22037/aaemj.v13i1.2512
- Primary Topic
- Trauma and Emergency Care Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00