  Tongue cancer is a common oral cavity malignancy that originates in the mouth
and throat. Much effort has been invested in improving its diagnosis,
treatment, and management. Surgical removal, chemotherapy, and radiation
therapy remain the major treatment for tongue cancer. The survival of patients
determines the treatment effect. Previous studies have identified certain
survival and risk factors based on descriptive statistics, ignoring the
complex, nonlinear relationship among clinical and demographic variables. In
this study, we utilize five cutting-edge machine learning models and clinical
data to predict the survival of tongue cancer patients after treatment.
Five-fold cross-validation, bootstrap analysis, and permutation feature
importance are applied to estimate and interpret model performance. The
prognostic factors identified by our method are consistent with previous
clinical studies. Our method is accurate, interpretable, and thus useable as
additional evidence in tongue cancer treatment and management.
