Optimal Machine Learning Models for Developing Prognostic Predictions in Patients With Advanced Cancer
Machine learning, particularly KSVM, has high predictive accuracy in palliative care when sufficient data are available. However, our findings suggest that traditional statistical models offer advantages in stability and interpretability, underscoring the importance of tailored model selection based on data characteristics.
Authors
- Jun Hamano (ORCID: https://orcid.org/0000-0003-0304-9881)
- Kenichi Hayashi (ORCID: https://orcid.org/0000-0002-3975-3005)
- Ayano Takeuchi (ORCID: https://orcid.org/0000-0002-0908-1850)
- Hiroyuki Nose
- Tomoya Keyaki
Institutions
- University of Tsukuba (JP)
- Keio University (JP)
- Chuo University (JP)
Publication Details
- Journal
- Cureus
- Published
- 2024-12-22
- DOI
- https://doi.org/10.7759/cureus.76227
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.21