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

Background: Osteosarcoma (OSC) is a rare but aggressive bone cancer and predicting survival outcomes remains a critical challenge in clinical practice. This study aims to evaluate the performance of machine learning models in predicting survival outcomes for OSC patients using regression and classification approaches. Methods: We analyzed data from 1471 OSC patients, with 55.7% male and a mean age of 26.13 years. Descriptive and survival analyses were performed using Python. Regression models (Linear Regression, LightGBM, XGBoost, and Random Forest) were deployed to predict survival as a continuous outcome and evaluated using mean absolute error (MAE), mean squared error (MSE), and Results: Among regression models, XGBoost performed best, achieving the lowest MAE (3.715) and MSE (20.788) and the highest Conclusion: 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.