Factors influencing the incidence of early gastric cancer: a bayesian network analysis.

ABSTRACT:

Background

This study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors.

Methods

Data were collected from early cancer screenings conducted at the People's Hospital of Lincang between 2022 and 2023. Initial variable selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) and Sliding Windows Sequential Forward Selection (SWSFS), and the screened variables and demographic characteristics features were used as variables for constructing the Bayesian network (BN) model. Subsequently, the performance of the models was evaluated, and the optimal model was

SUBMITTER: Li R 

PROVIDER: S-EPMC11927266 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature