A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.

ABSTRACT:

Background

Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them with conventional scores in predicting 10-year CVD risk for patients with breast cancer (BC), colorectal cancer (CRC), or lung cancer (LC).

Methods

We analyzed adults diagnosed with BC, CRC, or LC between 2005 and 2012. An machine learning (ML) Extreme Gradient Boosting algorithm ranked 40-50 covariates for predicting CVD for each cancer type using SHapley Additive exPlanations values. The top 10 ML-predictors were used to create predictive equations using logistic regression and compared with American College of Cardiology (ACC)/

SUBMITTER: Stabellini N 

PROVIDER: S-EPMC11878632 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature