Feature importance of the XGBoost model
tumor
REGRESSION, TUMOR
The XGBoost predictive model demonstrated the best performance among the four predictive models in accessing the 5‐year survival status of nonmetastatic CC patients. Afterward, we analyzed feature importance within the selected seven factors in the LASSO regression model. The higher values indicated more importance for the prediction targets, including the tumor stage based on the 2014 FIGO staging system (corresponding importance rate, 49.93%), surgery at the primary site (18.07%), use of RT (11.23%), use of CT (8.28%), age at diagnosis (6.32%), histology (3.17%), and marital status (3.09%) (Figure The XGBoost model was used to calculate the importance of seven features. XGBoost, extreme gradient boosting