Research on Survival Prediction Model of Bladder Cancer Patients Based on Machine Learning
This research focuses on constructing a survival prediction model based on Machine Learning to predict the 1-year,3-year,and 5-year survival rates for patients with Bladder Cancer,aid clinicians in accurately identifying patients with poor prognosis and assist in formulating clinical prognosis plans.Patient data is obtained from the Surveillance,Epidemiology,and End Results(SEER)database.The survival prediction model is constructed based on Logistic Regression(LR),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and the Cox proportional hazards model.The performance of the model is evaluated using the receiver operating characteristic curve and calibration curve on the training and validation sets.The experimental results demonstrate that GBDT exhibits high discrimination and good calibration in predicting the 1-year,3-year,and 5-year survival rates for BC patients.
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