Machine learning-based personalized risk prediction model for breast cancer-related lymphedema after surgery.

ABSTRACT:

Objective

Breast cancer-related lymphedema (BCRL) is a common postoperative complication that significantly impairs patients' quality of life. This study aims to develop a machine learning-based personalized risk prediction model for BCRL by integrating multimodal clinical and behavioral data, thereby providing scientific support for early identification and intervention in high-risk individuals.

Methods

Clinical and follow-up data were collected from patients who underwent breast cancer surgery between June 2020 and June 2025. A total of 38 variables were analyzed using the Least Absolute Shrinkage and Selection Operator (LASSO) method for feature selection. Nine machine learning algorithms were developed, and their performance was evaluated using metrics including accuracy

SUBMITTER: Peng X 

PROVIDER: S-EPMC12711468 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature