Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.

ABSTRACT: Risk prediction plays an important role in clinical cardiology research. Traditionally, most risk models have been based on regression models. While useful and robust, these statistical methods are limited to using a small number of predictors which operate in the same way on everyone, and uniformly throughout their range. The purpose of this review is to illustrate the use of machine-learning methods for development of risk prediction models. Typically presented as black box approaches, most machine-learning methods are aimed at solving particular challenges that arise in data analysis that are not well addressed by typical regression approaches. To illustrate these challenges, as well as how different methods can address them, we consider trying to predicting mortality after diagnosis of

SUBMITTER: Goldstein BA 

PROVIDER: S-EPMC5837244 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature