Construction and interpretation of weight-balanced enhanced machine learning models for predicting liver metastasis risk in colorectal cancer patients.

ABSTRACT:

Background

Colorectal cancer (CRC) is a major contributor to cancer-related mortality, with liver metastases developing in approximately 25% of affected individuals. The presence of liver metastasis significantly deteriorates the prognosis for patients. The objective of this study is to predict liver metastasis in CRC patients by developing machine learning (ML)-based models, thereby aiding clinicians in the decision-making process for appropriate interventions.

Methods

Retrospective analysis was performed using the Surveillance, Epidemiology, and End Results (SEER) database, and cases with CRC from 2010 to 2015 were extracted to the downstream analysis. Logistic regression (LR), Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Catego

SUBMITTER: Ding Q 

PROVIDER: S-EPMC11822177 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature