Population-based colorectal cancer risk prediction using a SHAP-enhanced LightGBM model.

ABSTRACT:

Background

Colorectal cancer (CRC) is a highly frequent cancer worldwide, and early detection and risk stratification playing a critical role in reducing both incidence and mortality. we aimed to develop and validate a machine learning (ML) model using clinical data to improve CRC identification and prognostic evaluation.

Methods

We analyzed multicenter datasets comprising 676 CRC patients and 410 controls from Guigang City People's Hospital (2020-2024) for model training/internal validation, with 463 patients from Laibin City People's Hospital for external validation. Seven ML algorithms were systematically compared, with Light Gradient Boosting Machine (LightGBM) ultimately selected as the optimal framework. Model performance was rigorously assessed through area under the

SUBMITTER: Du G 

PROVIDER: S-EPMC12310463 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature