Development of a machine learning-based model for prognostic prediction in melanoma.

ABSTRACT: Melanoma is an aggressive skin cancer associated with a poor prognosis, making survival time a primary concern for patients. This study applies five machine learning models to predict survival rates for melanoma patients, aiming to improve prognostic accuracy and support clinical decision-making. Melanoma patient data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Five machine learning models-Random Forest, Decision Tree, XGBoost, CatBoost, and LightGBM-were applied to predict 1-year, 3-year, and 5-year survival rates for melanoma patients. The CatBoost model was selected for its superior performance and evaluated using the Area Under the Receiver Operating Characteristic Curve (AUC), confusion matrix, calibration curves, and decision curve analysis (D

SUBMITTER: Hu E 

PROVIDER: S-EPMC12753756 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature