Abstract
Cox regression model is used for modelling censored data to investigate the association between the survival time and covariates. It is important to assess the fit of Cox regression model since it has a key assumption called proportional hazards. Violation of this assumption induces an invalid model and changes the interpretation of the results. When the objective is the risk prediction, various machine learning methods can be good alternatives to Cox regression model due to their flexible structure. In this study, Turkish breast cancer data set is used to compare the predictive performance of Cox regression model and ensemble machine learning methods. Integrated Brier score is used to measure the predictive performance of candidate models. Based on case study results, machine learning methods are promising alternatives for survival prediction.
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