Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach.

Background: Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques.

Methods: Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms.