Machine learning with sparse nutrition data to improve cardiovascular mortality risk prediction in the USA using nationally randomly sampled data.

ABSTRACT: OBJECTIVES:We aimed to test whether or not adding (1) nutrition predictor variables and/or (2) using machine learning models improves cardiovascular death prediction versus standard Cox models without nutrition predictor variables. DESIGN:Retrospective study. SETTING:Six waves of Survey (NHANES) data collected from 1999 to 2011 linked to the National Death Index (NDI). PARTICIPANTS:29 390 participants were included in the training set for model derivation and 12 600 were included in the test set for model evaluation. Our study sample was approximately 20% black race and 25% Hispanic ethnicity. PRIMARY AND SECONDARY OUTCOME MEASURES:Time from NHANES interview until the minimum of time of cardiovascular death or censoring. RESULTS:A standard risk model excluding nutrition data overestimated

SUBMITTER: Rigdon J 

PROVIDER: S-EPMC6924849 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature