Performance Comparison of 10 State-of-the-Art Machine Learning Algorithms for Outcome Prediction Modeling of Radiation-Induced Toxicity
Our results show that there is no best algorithm for all data sets. Therefore, it is important to compare multiple algorithms when training an outcome prediction model on a new data set. The graphical user interface created for this study automatically compares the performance of these 11 algorithms for any data set.
Authors
- Michael Kevin Rooney (ORCID: https://orcid.org/0000-0002-2860-4653)
- Kelsey L. Corrigan (ORCID: https://orcid.org/0000-0003-0314-6972)
- Emma B. Holliday (ORCID: https://orcid.org/0000-0002-4362-461X)
- Raymond Mumme (ORCID: https://orcid.org/0000-0001-7383-6122)
- Matthew S. Ning (ORCID: https://orcid.org/0000-0002-2932-5584)
- Laurence E. Court (ORCID: https://orcid.org/0000-0002-3241-6145)
- Jack Duryea (ORCID: https://orcid.org/0000-0002-5539-5885)
- S. Nair (ORCID: https://orcid.org/0009-0001-0371-635X)
- Ramon M. Salazar (ORCID: https://orcid.org/0000-0002-1883-9029)
- Joshua S. Niedzielski (ORCID: https://orcid.org/0000-0002-7666-5039)
- Ting Xu (ORCID: https://orcid.org/0000-0002-7851-3389)
- Prajnan Das (ORCID: https://orcid.org/0000-0002-3845-0707)
- Zhongxing Liao (ORCID: https://orcid.org/0000-0002-2224-9478)
- Brian De (ORCID: https://orcid.org/0000-0003-3468-3359)
- Alexandra O. Leone (ORCID: https://orcid.org/0000-0002-2497-7209)
Institutions
- The University of Texas MD Anderson Cancer Center (US)
Publication Details
- Journal
- Advances in Radiation Oncology
- Published
- 2024-11-13
- DOI
- https://doi.org/10.1016/j.adro.2024.101675
- Citations
- 5
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 3.04
Funders
- Varian Medical Systems
- University of Texas MD Anderson Cancer Center