Prognostic prediction of breast cancer patients using machine learning models: a retrospective analysis.

ABSTRACT:

Background

Breast cancer is a common and complex disease, with various clinical features affecting prognosis. Accurate prediction of prognosis is essential for guiding personalized treatment strategies. This study aimed to develop machine learning models for predicting prognosis in breast cancer patients using retrospective data.

Methods

A total of 6,477 patients from Affiliated Sir Run Run Shaw Hospital were included, and their electronic medical records (EMRs) were thoroughly examined to identify 15 clinical features significantly associated with breast cancer survival. We employed eight different machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), to develop and evaluat

SUBMITTER: Song X 

PROVIDER: S-EPMC11480873 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature