Comparison of machine learning methods versus traditional Cox regression for survival prediction in cancer using real-world data: a systematic literature review and meta-analysis
ML models had similar performance compared with CPH models in predicting cancer survival outcomes. Although this systematic review highlights the promising use of ML to improve the quality of care in oncology, findings from this review also suggest opportunities to improve ML reporting transparency. Future systematic reviews should focus on the comparative performance between specific ML models and CPH regression in time-to-event outcomes in specific type of cancer or other disease areas.
Authors
- John P. Bentley (ORCID: https://orcid.org/0000-0002-9241-6833)
- Jieni Li (ORCID: https://orcid.org/0000-0002-8386-4116)
- Mai Suan Li (ORCID: https://orcid.org/0000-0001-7021-7916)
- Kaustuv Bhattacharya (ORCID: https://orcid.org/0000-0001-8857-1817)
- Arman Arabshomali (ORCID: https://orcid.org/0000-0001-7243-6573)
- Shadi Bazzazzadehgan (ORCID: https://orcid.org/0000-0002-1407-4727)
- Yinan Huang (ORCID: https://orcid.org/0000-0002-8909-0385)
Institutions
- University of Mississippi (US)
- University of Houston (US)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1186/s12874-025-02694-z
- Citations
- 3
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 7.44