HIGHLIGHTS
SUMMARY
Breast cancer represents the most common cancer disease in women and also is the leading cause of cancer-related death. The authors proposed a preliminary model to predict the complications of radiotherapy after mastectomy trained on with the purpose of undertaking the best treatment course and reconstructive timing for each patient, that is, to identify patients with a higher predicted risk of postoperative reconstructive complications. XGBoost stands for "Extreme Gradient Boosting", and it is a decision tree ensemble learning algorithm similar to random forest, which combines multiple machine_learning algorithms to obtain a better model . . .
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