  As we gain access to a greater depth and range of health-related information
about individuals, three questions arise: (1) Can we build better models to
predict individual-level risk of ill health? (2) How much data do we need to
effectively predict ill health? (3) Are new methods required to process the
added complexity that new forms of data bring? The aim of the study is to apply
a machine learning approach to identify the relative contribution of personal,
social, health-related, biomarker and genetic data as predictors of future
health in individuals. Using longitudinal data from 6830 individuals in the UK
from Understanding Society (2010-12 to 2015-17), the study compares the
predictive performance of five types of measures: personal (e.g. age, sex),
social (e.g. occupation, education), health-related (e.g. body weight, grip
strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide
polymorphisms (SNPs). The predicted outcome variable was limiting long-term
illness one and five years from baseline. Two machine learning approaches were
used to build predictive models: deep learning via neural networks and XGBoost
(gradient boosting decision trees). Model fit was compared to traditional
logistic regression models. Results found that health-related measures had the
strongest prediction of future health status, with genetic data performing
poorly. Machine learning models only offered marginal improvements in model
accuracy when compared to logistic regression models, but also performed well
on other metrics e.g. neural networks were best on AUC and XGBoost on
precision. The study suggests that increasing complexity of data and methods
does not necessarily translate to improved understanding of the determinants of
health or performance of predictive models of ill health.
