  Background: Cardiovascular diseases (CVDs) are among the leading causes of
death worldwide. Predictive scores providing personalised risk of developing
CVD are increasingly used in clinical practice. Most scores, however, utilise a
homogenous set of features and require the presence of a physician.
  Objective: The aim was to develop a new risk model (DiCAVA) using statistical
and machine learning techniques that could be applied in a remote setting. A
secondary goal was to identify new patient-centric variables that could be
incorporated into CVD risk assessments.
  Methods: Across 466,052 participants, Cox proportional hazards (CPH) and
DeepSurv models were trained using 608 variables derived from the UK Biobank to
investigate the 10-year risk of developing a CVD. Data-driven feature selection
reduced the number of features to 47, after which reduced models were trained.
Both models were compared to the Framingham score.
  Results: The reduced CPH model achieved a c-index of 0.7443, whereas DeepSurv
achieved a c-index of 0.7446. Both CPH and DeepSurv were superior in
determining the CVD risk compared to Framingham score. Minimal difference was
observed when cholesterol and blood pressure were excluded from the models
(CPH: 0.741, DeepSurv: 0.739). The models show very good calibration and
discrimination on the test data.
  Conclusion: We developed a cardiovascular risk model that has very good
predictive capacity and encompasses new variables. The score could be
incorporated into clinical practice and utilised in a remote setting, without
the need of including cholesterol. Future studies will focus on external
validation across heterogeneous samples.
