  In this work, we investigate the importance of ethnicity in colorectal cancer
survivability prediction using machine learning techniques and the SEER cancer
incidence database. We compare model performances for 2-year survivability
prediction and feature importance rankings between Hispanic, White, and mixed
patient populations. Our models consistently perform better on single-ethnicity
populations and provide different feature importance rankings when trained in
different populations. Additionally, we show our models achieve higher Area
Under Curve (AUC) score than the best reported in the literature. We also apply
imbalanced classification techniques to improve classification performance when
the number of patients who have survived from colorectal cancer is much larger
than who have not. These results provide evidence in favor for increased
consideration of patient ethnicity in cancer survivability prediction, and for
more personalized medicine in general.
