Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data.

ABSTRACT: Colorectal cancer (CRC) is a prevalent malignant tumor that presents significant challenges to both public health and healthcare systems. The aim of this study was to develop a machine learning model based on five years of clinical follow-up data from CRC patients to accurately identify individuals at risk of poor prognosis. This study included 411 CRC patients who underwent surgery at Yixing Hospital and completed the follow-up process. A modeling dataset containing 73 characteristic variables was established by collecting demographic information, clinical blood test indicators, histopathological results, and additional treatment-related information. Decision tree, random forest, support vector machine, and extreme gradient boosting (XGBoost) models were selected for modeling based on the

SUBMITTER: Xiao B 

PROVIDER: S-EPMC11750956 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature