Limitation and future improvement
There are limitations to this study. First, data variability including transformations and normalizations is the major challenge for the widespread use of ML algorithms. Second, the SEER database identified only 11 features per patient, which would limit the predictive power of ML algorithms, not only for the XGBoost model. To build more accurate and effective predictive models in the future, we need to introduce more features for analysis. Third, theories of different ML algorithms are obscure and intricate. For clinicians to utilize these models efficiently, they need to be not only intelligible but also capable of estimating their uncertainties.