Random survival forest algorithm for risk stratification and survival prediction in gastric neuroendocrine neoplasms.

ABSTRACT: This study aimed to construct and assess a machine-learning algorithm designed to forecast survival rates and risk stratification for patients with gastric neuroendocrine neoplasms (gNENs) after diagnosis. Data on patients with gNENs were extracted and randomly divided into training and validation sets using the Surveillance, Epidemiology, and End Results database. We developed a prediction model using 10 machine learning algorithms across 101 combinations to forecast cancer-related mortality in patients with gNENs, selecting the best model using the highest mean over a sequence of time-dependent area under the receiver operating characteristic (ROC) curve (AUC). The performance of the final model was assessed through time-dependent ROC curves for discrimination and calibration curves for

SUBMITTER: Liao T 

PROVIDER: S-EPMC11541730 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature