Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

ABSTRACT: Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival fores

SUBMITTER: Buyrukoglu G 

PROVIDER: S-EPMC11323082 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature