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

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 risk nearly two-fold (calibration slope of predicted vs true risk: 0.53 (95% CI: 0.50 to 0.55)) with moderate discrimination (C-statistic: 0.87 (0.86 to 0.89)). Nutrition data alone failed to improve performance while machine learning alone improved calibration to 1.18 (0.92 to 1.44) and discrimination to 0.91 (0.90 to 0.92). Both together substantially improved calibration (slope: 1.01 (0.76 to 1.27)) and discrimination (C-statistic: 0.93 (0.92 to 0.94)). CONCLUSION: Our results indicate that the inclusion of nutrition data with available machine learning algorithms can substantially improve cardiovascular risk prediction.