Predictive Models Using Interpretable Machine Learning for Cardiovascular Survival in Breast Cancer Patients with Secondary Malignancies: Insights from a SEER Analysis

1

Breast cancer survivors face increased risks of second primary cancers and cardiovascular disease due to treatment-related complications.
2

Existing research has primarily focused on either second primary cancers or cardiovascular risks, lacking integrated predictive models for breast cancer patients.
3

Machine learning models, including RSF, SVM, XGBoost, and DeepSurv, offer superior capabilities for predicting cardiovascular-specific survival in breast cancer patients.
4

The study utilized data from the SEER database to develop and compare predictive models for cardiovascular mortality in breast cancer patients with secondary malignancies.
5

An interactive web-based prediction tool was created to facilitate personalized risk assessment and dynamic health management for breast cancer patients.

1