Machine learning studies for predicting 5-year renal cell cancer survival: a multicenter study.

ABSTRACT:

Background

Renal cell carcinoma (RCC) is biologically heterogeneous, complicating prognosis and treatment. Our study aims to clarify prognostic factors and support evidence-based clinical decision-making in RCC management.

Methods

This study analyzed clinical data of RCC patients diagnosed between 2010 and 2021 from the Surveillance, Epidemiology, and End Results database. Prognostic factors associated with patient outcomes were selected using least absolute shrinkage and selection operator regression and Boruta. Seven machine learning algorithms were employed to construct predictive models for 5-year survival in RCC patients. Shapley Additive Explanations (SHAP) analysis was utilized to visually interpret the model's predictions. Model performance was evaluated using the fo

SUBMITTER: Wang M 

PROVIDER: S-EPMC12683448 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature