Key result
Machine learning offers an alternative to traditional regression for complex CV risk prediction.
Why the study?
Traditional regression-based cardiovascular risk models are limited to a small number of predictors operating uniformly, prompting the need to illustrate machine-learning methods to address data analysis challenges.
How can machine learning methods address analytic challenges in cardiovascular risk prediction compared to traditional regression techniques?
How can machine learning methods address analytic challenges in cardiovascular risk prediction compared to traditional regression techniques?
This review provides an introduction to applying machine learning techniques to overcome the limitations of traditional regression models in cardiovascular risk prediction.
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Should not yet alter clinical cardiovascular risk prediction; leaves open machine learning's incremental value pending prospective comparative studies.
Goldstein et al. (2016) conducted a review in Acute myocardial infarction. Machine learning methods vs. Regression models was evaluated on Mortality. Machine learning methods provide alternative approaches to traditional regression models for addressing complex analytic challenges in cardiovascular risk prediction.
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