Invasive and non-invasive variables prediction models for cardiovascular disease-specific mortality between machine learning vs. traditional statistics.

ABSTRACT: This study examined the predictive performance of cardiovascular disease (CVD)-specific mortality using traditional statistical and machine learning models with non-invasive indicators, and assessed whether adding blood lipid profiles improves prediction. Data were from 1,749,444 Korean adults (44.7% female) from the Korea Medical Institute. Non-invasive predictors included sex, age, waist-to-height ratio, diabetes, hypertension, and physical activity; invasive variables included triglycerides, fasting glucose, and cholesterol. CVD-specific mortality was tracked over a 10-year follow-up. We applied Cox proportional hazards models (with and without elastic net penalty), Random Survival Forest, Gradient Boosting Survival, and Survival Tree models. Predictive performance was compared using ar

SUBMITTER: Choi S 

PROVIDER: S-EPMC12508446 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature