![]() Our prediction model had excellent accuracy and is therefore useful in clinical settings. XGBoost is an effective algorithm for predicting 5-year survival of osteosarcoma patients. Decision curve analyses proved the model could be used to support clinical decisions. The accuracy of the prediction model was excellent both in the training set (area under the ROC curve = 0.977) and the validation set (AUC = 0.911). Receiver operating characteristic (ROC) curve and decision curve analyses were performed to evaluate the prediction. ![]() Characteristics selected via survival analyses were used to construct the model. The study population was 835 and was divided into the training set ( n = 668) and validation set ( n = 167). We identified cases of osteosarcoma in the Surveillance, Epidemiology, and End Results (SEER) Research Database and excluded substandard samples. Therefore, we aimed to construct an artificial intelligence (AI) model for predicting the 5-year survival of osteosarcoma patients by using extreme gradient boosting (XGBoost), a large-scale machine-learning algorithm. However, there is still no prediction model with a high accuracy rate for osteosarcoma. ![]() Survival rate prediction is important for improving prognosis and planning therapy. Osteosarcoma is the most common bone malignancy, with the highest incidence in children and adolescents.
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