The prediction of distant metastasis risk for male breast cancer patients based on an interpretable machine learning model.

ABSTRACT:

Objectives

This research was designed to compare the ability of different machine learning (ML) models and nomogram to predict distant metastasis in male breast cancer (MBC) patients and to interpret the optimal ML model by SHapley Additive exPlanations (SHAP) framework.

Methods

Four powerful ML models were developed using data from male breast cancer (MBC) patients in the SEER database between 2010 and 2015 and MBC patients from our hospital between 2010 and 2020. The area under curve (AUC) and Brier score were used to assess the capacity of different models. The Delong test was applied to compare the performance of the models. Univariable and multivariable analysis were conducted using logistic regression.

Results

Of 2351 patients were analyzed; 168 (7.1%) had dista

SUBMITTER: Zhao X 

PROVIDER: S-EPMC10120176 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature