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June 12, 2026European Journal of Preventive CardiologyOpen Access

Enhancing cardiovascular risk prediction with neighbourhood determinants of health: a machine learning analysis in a nationwide population-based cohort of 1.8 million patients

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Key result

Machine learning survival models perform marginally worse than Cox models for 10-year CVD risk prediction.

  • n=1,776,865

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

JBJ R G BrownQueen Mary University of LondonPBP J BaptisteQueen Mary University of LondonHHH HajmohammadiQueen Mary University of London

Discussion

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Implication

Large cohort data confirm high first-time CVD incidence; leaves open whether neighbourhood determinants meaningfully improve clinical risk models.

Key Points

  • This study aims to improve cardiovascular disease risk prediction by integrating neighbourhood health determinants and comparing traditional and machine learning models.
  • Utilized longitudinal data from the Secure Anonymised Information Linkage (SAIL) Databank of 1.8 million patients.
  • Developed a Cox proportional hazards model to estimate 10-year CVD risk, incorporating social determinants like area deprivation and air pollution.
  • Compared performance with machine learning models including Random Forest, Support Vector Machine, and Neural Network.
  • Cox proportional hazards model achieved a C-index of 0.8082, outperforming machine learning models.
  • First-time CVD events recorded at 254,040 (14.3%) during the follow-up period.
  • Best AUC at 10 years for the Cox model was 0.8528, indicating superior predictive performance.

Study Design

Type

Cohort (n=1,776,865)

Multicenter

Yes

Structured PICO

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?

P
Population
1,776,865 adults aged 25-84 years registered with general practices in Wales, followed for a median of 15.2 years to predict incident CVD events.
E
Exposure
Machine learning survival models (Random Forest, Support Vector Machine [SVM], Gradient Boosting, Penalised Cox Regression, Neural Network) incorporating standard QRISK predictors plus area-level deprivation (IMD) and residential air pollution (NO2).
C
Comparator
Cox proportional hazards model incorporating the same clinical, social, and environmental predictors.
O
Outcome
Model performance for predicting 10-year incident cardiovascular disease (CVD) events, evaluated using concordance index (C-index), Brier score, and area under the curve (AUC) at 10-years.

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.

Limitations

  • Having initially used a 1% sample, our results may be biased.
  • Having initially used a 1% sample, results may be biased

Cite This Study

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.

synapsesocial.com/papers/6a2bd1386550ea4541ffe9b1https://doi.org/10.1093/eurjpc/zwag249.192
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Benchmarking survival machine learning models for 10-year cardiovascular disease risk prediction using large-scale electronic health records2026 · 3 citations
  2. 2Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study2023 · 68 citations
  3. 3Enhanced cardiovascular disease risk prediction using integrated machine learning models: a study from the UK Biobank cohort2026 · 3 citations
  4. 4The uncertainty with using risk prediction models for individual decision making: an exemplar cohort study examining the prediction of cardiovascular disease in English primary care2019 · 55 citations
  5. 5Accurate machine learning-based CVD risk prediction in primary care may reduce the need for routine healthcare checks2026