Prediction of renal cell carcinoma: Development and validation of machine learning model.

ABSTRACT: Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient outcomes. This study aims to construct and validate an RCC prediction model for at-risk individuals using machine learning (ML) based on routine clinical data. Data from the Quanzhou First Hospital Affiliated with Fujian Medical University between March 2014 and March 2024 were retrospectively collected, with 70% randomly assigned to the training cohort and 30% to the validation cohort. Univariate and hierarchical clustering methods were employed to identify discriminatory features to enable optimal ML algorithm selection. The performance of 7 kinds of ML algorithms-based models was evaluated based on sensitivity (recall), accuracy, F1-score,

SUBMITTER: Zheng T 

PROVIDER: S-EPMC12851772 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature