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As we gain access to a greater depth and range of health-related informationabout individuals, three questions arise: (1) Can we build better models topredict individual-level risk of ill health? (2) How much data do we need toeffectively predict ill health? (3) Are new methods required to process theadded complexity that new forms of data bring? The aim of the study is to applya machine learning approach to identify the relative contribution of personal,social, health-related, biomarker and genetic data as predictors of futurehealth in individuals. Using longitudinal data from 6830 individuals in the UKfrom Understanding Society (2010-12 to 2015-17), the study compares thepredictive performance of five types of measures: personal (e.g. age, sex),social (e.g. occupation, education), health-related (e.g. body weight, gripstrength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotidepolymorphisms (SNPs). The predicted outcome variable was limiting long-termillness one and five years from baseline. Two machine learning approaches wereused to build predictive models: deep learning via neural networks and XGBoost(gradient boosting decision trees). Model fit was compared to traditionallogistic regression models. Results found that health-related measures had thestrongest prediction of future health status, with genetic data performingpoorly. Machine learning models only offered marginal improvements in modelaccuracy when compared to logistic regression models, but also performed wellon other metrics e.g. neural networks were best on AUC and XGBoost onprecision. The study suggests that increasing complexity of data and methodsdoes not necessarily translate to improved understanding of the determinants ofhealth or performance of predictive models of ill health.

Evaluating the performance of personal, social, health-related,  biomarker and genetic data for predicting an individuals future health using  machine learning: A longitudinal analysis