Predicting Survival of Tongue Cancer Patients by Machine Learning Models

TL;DR

This study employs advanced machine learning models on clinical data to accurately predict tongue cancer patient survival, providing interpretable results that support treatment decisions.

Contribution

It introduces the use of five machine learning models with validation techniques to predict survival and identify prognostic factors in tongue cancer, enhancing clinical decision-making.

Findings

Models achieved high predictive accuracy.

Identified prognostic factors align with clinical studies.

Method is interpretable and applicable in treatment planning.

Abstract

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.