Stage-Specific Survivability Prediction Models across Different Cancer Types.

For all cancer types, survivability rates vary widely across different stages of cancer. But survivability prediction models built in past were trained using examples of all stages together and were also evaluated on all stages together. In this work, for ten cancer types and using three machine learning methods, we built survivability prediction models trained on each stage separately and compared their performance with the traditional models trained on all stages together. For both kinds of models, the evaluation was done on each stage separately as well as on all stages together. Our results show that for most cancer types the stages are sufficiently different from each other that it is best to build survivability prediction models separately for each stage. We also found that evaluating survivability prediction models on all stages together, as was done previously, overestimates performance for all the stages on all cancer types.