CONCLUSIONS
tumor
REGRESSION, TUMOR
In the current investigation, we first selected seven readily available factors including age at diagnosis, marital status, histology, tumor stage, surgery at the primary site, RT, and CT on the basis of the LASSO regression model. To evaluate the accuracy of the predictions for nonmetastatic CC patients' 5‐year survival status, four models were developed. Owning to the superiority of its inherent algorithm, XGBoost model was found to demonstrate satisfactory prediction accuracy according to AUC analyses and DCA curves in comparison with other algorithms. In addition, a variable importance analysis revealed that tumor stage based on the 2014 FIGO system of staging was the most significant feature in the XGBoost model. We propose that the developed ML algorithms like XGBoost could be constructed as an efficient clinical support tool for helping clinicians with the better management of nonmetastatic CC patients in the future.