Authors : Francis Mawutor Amuyao

Volume/Issue : Volume 11 - 2026, Issue 6 - June

---

Google Scholar : https://tinyurl.com/2bbbsjkj

Scribd : https://tinyurl.com/3ny5c7xj

DOI : https://doi.org/10.38124/ijisrt/26jun1500

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

---

Abstract : Breast cancer recurrence risk prediction informs adjuvant treatment decisions and follow-up planning. Molecular subtypes capture biologically distinct risk profiles, yet whether machine learning (ML) survival methods offer consistent advantages over Cox proportional hazards (PH) modelling across subtypes remains unclear. We analysed 1,964 breast cancer patients across five molecular subtypes. Penalised Cox PH, Random Survival Forest (RSF), and Gradient Boosting Survival (GBS) models were developed for recurrence-free survival (RFS) prediction using discrimination, calibration, decision curve analysis, and subtype-stratified SHAP explainability.

Keywords : Breast Cancer; Recurrence Prediction; Survival Analysis; Molecular Subtype; Random Survival Forest; SHAP; METABRIC; Cox Proportional Hazards; External Validation.

References :

1. G.S. Collins, P. Dhiman, C.L.A. Navarro, et al., “Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI),” BMJ, vol. 375, p. n2334, 2021.
2. B. Van Calster, D.J. McLernon, M. van Smeden, et al., “Calibration: the Achilles heel of predictive analytics,” BMC Medicine, vol. 17, no. 1, p. 230, 2019.
3. A.J. Vickers, E.B. Elkin, “Decision curve analysis: a novel method for evaluating prediction models,” Medical Decision Making, vol. 26, no. 6, pp. 565–574, 2006.
4. H. Ishwaran, U.B. Kogalur, E.H. Blackstone, M.S. Lauer, “Random survival forests,” Annals of Applied Statistics, vol. 2, no. 3, pp. 841–860, 2008.
5. S. Polsterl, “scikit-survival: A library for time-to-event analysis built on top of scikit-learn,” Journal of Machine Learning Research, vol. 21, no. 212, pp. 1–6, 2020.
6. S.M. Lundberg, S.-I. Lee, “A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems, vol. 30, 2017.
7. C. Curtis, S.P. Shah, S.-F. Chin, et al., “The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups,” Nature, vol. 486, no. 7403, pp. 346– 352, 2012.
8. C. Davidson-Pilon, “lifelines: survival analysis in Python,” Journal of Open Source Software, vol. 4, no. 40, p. 1317, 2019.
9. P.M. Grambsch, T.M. Therneau, “Proportional hazards tests and diagnostics based on weighted residuals,” Biometrika, vol. 81, no. 3, pp. 515–526, 1994.
10. F.E. Harrell, R.M. Califf, D.B. Pryor, K.L. Lee, R.A. Rosati, “Evaluating the yield of medical tests,” JAMA, vol. 247, no. 18, pp. 2543–2546, 1982.