Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.

ABSTRACT: This study aimed to develop a machine learning model for accurately predicting postoperative VTE risk in these patients. The data of this study retrospectively collected the inpatients of cervical cancer in the Affiliated Cancer Hospital of Chongqing University from January 2020 to December 2023. We utilized 1,044 observations and six variables to develop seven machine learning (ML) models and selected the best-performing model for assessing postoperative venous thromboembolism (VTE) risk. The models were evaluated using ROC, PR, and DCA curves, and the prediction of the model's process was explained using SHAP values. Among 1,044 postoperative cervical cancer patients, 82 (7.85%) developed VTE. Seven machine learning algorithms were developed and evaluated, with the random forest (RF) mod

SUBMITTER: Chen C 

PROVIDER: S-EPMC12749271 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature