Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
University of California, Los Angeles · University of Cambridge · +5 more institutions
Abstract
BACKGROUND: Identifying people at risk of cardiovascular diseases (CVD) is a cornerstone of preventative cardiology. Risk prediction models currently recommended by clinical guidelines are typically based on a limited number of predictors with sub-optimal performance across all patient groups. Data-driven techniques based on machine learning (ML) might improve the performance of risk predictions by agnostically discovering novel risk predictors and learning the complex interactions between them. We tested (1) whether ML techniques based on a state-of-the-art automated ML framework (AutoPrognosis) could improve CVD risk prediction compared to traditional approaches, and (2) whether considering non-traditional…
Citation impact
- FWCI
- 89.94
- Percentile
- 100%
- References
- 51
Authors
5- AMAhmed M. AlaaCorresponding
University of California, Los Angeles
- TBThomas Bolton
University of Cambridge, National Institute for Health Research, Genomics (United Kingdom)
- EDEmanuele Di Angelantonio
University of Cambridge, National Institute for Health Research, Genomics (United Kingdom)
- JHJames H.F. Rudd
University of Cambridge, Cambridge University Hospitals NHS Foundation Trust
- MVMihaela van der Schaar
University of California, Los Angeles, University of Oxford, The Alan Turing Institute
Topics & keywords
- Framingham Risk Score
- Receiver operating characteristic
- Biobank
- Predictive modelling
- Random forest
- Machine learning
- Proportional hazards model
- Medicine
- Good health and well-being