Key result
Machine learning survival models perform marginally worse than Cox models for 10-year CVD risk prediction.
Why the study?
Established CVD risk prediction tools rely on conventional predictors without non-traditional determinants and traditional regression, while machine learning may enhance performance.
Do machine learning survival models improve 10-year cardiovascular risk prediction compared to a Cox proportional hazards model when incorporating social and environmental determinants in a primary care population?
Population
1,776,865 adults aged 25–84 years registered with general practices across Wales
Comparison
Cox proportional hazards model vs machine learning survival models incorporating social and environmental determinants
Design
Population-based cohort study
Follow-up
Median 15.2 years
Authors
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Large cohort data confirm high first-time CVD incidence; leaves open whether neighbourhood determinants meaningfully improve clinical risk models.
Cohort (n=1,776,865)
Yes
Do machine learning survival models improve 10-year cardiovascular risk prediction compared to a Cox proportional hazards model when incorporating social and environmental determinants in a primary care population?
Traditional Cox proportional hazards models performed marginally better than machine learning approaches for predicting 10-year cardiovascular risk when incorporating social and environmental determinants in a large primary care cohort.
Brown et al. (2026) conducted a cohort in Cardiovascular disease (n=1,776,865). Machine learning survival models vs. Cox proportional hazards model was evaluated on 10-year CVD risk prediction performance (C-index). Machine learning survival models (Kernal SVM C-index 0.7896) performed marginally worse than a Cox proportional hazards model (C-index 0.8082) for predicting 10-year CVD risk.
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