Using machine learning to identify risk factors for pancreatic cancer: a retrospective cohort study of real-world data.

ABSTRACT:

Objectives

This study aimed to identify the risk factors for pancreatic cancer through machine learning.

Methods

We investigated the relationships between different risk factors and pancreatic cancer using a real-world retrospective cohort study conducted at West China Hospital of Sichuan University. Multivariable logistic regression, with pancreatic cancer as the outcome, was used to identify covariates associated with pancreatic cancer. The machine learning model extreme gradient boosting (XGBoost) was adopted as the final model for its high performance. Shapley additive explanations (SHAPs) were utilized to visualize the relationships between these potential risk factors and pancreatic cancer.

Results

The cohort included 1,982 patients. The median ages for pancreat

SUBMITTER: Su N 

PROVIDER: S-EPMC11617206 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature