Decision tree-based machine learning algorithm for prediction of acute radiation esophagitis
The decision tree model demonstrates high accuracy in predicting radiation-induced esophagitis grades while maintaining clinical interpretability. This approach offers potential for treatment optimization and personalized risk assessment in radiotherapy planning. The model's transparency and reliability make it a promising tool for clinical decision support in radiation oncology.
Authors
- Asghar Mesbahi (ORCID: https://orcid.org/0000-0003-0273-3526)
- Mostafa Alizade‐Harakiyan (ORCID: https://orcid.org/0000-0003-3415-4713)
- Amin Khodaei (ORCID: https://orcid.org/0000-0002-8175-5039)
- Hamed Zamani (ORCID: https://orcid.org/0000-0002-0800-3340)
- Ali Yousefi (ORCID: https://orcid.org/0000-0002-2405-9934)
Institutions
- Tabriz University of Medical Sciences (IR)
- University of Tabriz (IR)
Publication Details
- Journal
- Biochemistry and Biophysics Reports
- Published
- 2025-03-30
- DOI
- https://doi.org/10.1016/j.bbrep.2025.101991
- Citations
- 4
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.07