A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank.

Aims: A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity.

Methods And Results: The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements.