Machine learning algorithms and web-based prognostic tool for different histological subtypes of osteosarcoma: a retrospective cohort

Abstract

Machine learning models, particularly XGBoost for regression and Logistic Regression for classification, demonstrate strong potential for predicting survival outcomes in OSC patients. These findings underscore the utility of machine learning in enhancing clinical decision-making and suggest avenues for future research, including incorporating additional clinical variables and advanced modeling techniques to improve long-term survival predictions.

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