Predicting Survival of Tongue Cancer Patients by Machine Learning Models
This work addresses survival prediction for tongue cancer patients, offering an incremental improvement by applying existing machine learning methods to clinical data for better treatment management.
The study tackled predicting survival of tongue cancer patients after treatment using machine learning models on clinical data, achieving accurate and interpretable results that align with known prognostic factors.
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.