Stage-specific predictive models for breast cancer survivability.

BACKGROUND: Survivability rates vary widely among various stages of breast cancer. Although machine learning models built in past to predict breast cancer survivability were given stage as one of the features, they were not trained or evaluated separately for each stage.
OBJECTIVE: To investigate whether there are differences in performance of machine learning models trained and evaluated across different stages for predicting breast cancer survivability.
METHODS: Using three different machine learning methods we built models to predict breast cancer survivability separately for each stage and compared them with the traditional joint models built for all the stages. We also evaluated the models separately for each stage and together for all the stages. RESULTS AND
CONCLUSIONS: Our results show that the most suitable model to predict survivability for a specific stage is the model trained for that particular stage. In our experiments, using additional examples of other stages during training did not help, in fact, it made it worse in some cases. The most important features for predicting survivability were also found to be different for different stages. By evaluating the models separately on different stages we found that the performance widely varied across them. We also demonstrate that evaluating predictive models for survivability on all the stages together, as was done in the past, is misleading because it overestimates performance. Copyright Â