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Subtype-Dependent Performance of Cox and Machine Learning Survival Models for Recurrence Prediction in Breast Cancer: Development and External Validation Using Public Clinical Data

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  • Francis Mawutor Amuyao

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.

Suggested Citation

  • Francis Mawutor Amuyao, 2026. "Subtype-Dependent Performance of Cox and Machine Learning Survival Models for Recurrence Prediction in Breast Cancer: Development and External Validation Using Public Clinical Data," International Journal of Innovative Science and Research Technology (IJISRT), IJISRT Publication, vol. 11(06), pages 3298-3304, July.
    • Handle: RePEc:cvr:ijisrt:2026:06:ijisrt26jun1500
    DOI: https://doi.org/10.38124/ijisrt/26jun1500
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