Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants
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.
Record details
Published: 15 May 2019
Source: OpenAlex
Category: Research
Topics: Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (15 May 2019), “Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK Biobank participants,” evidence record 8506, https://ethics.ai/record/8506 (originally published by OpenAlex).
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