A machine learning framework for cardiovascular health prediction modeling the interplay between various environmental, neighborhood and socio- economic features: a German-wide application

Abstract

Although there is strong evidence that human health and well-being are strongly associated with the exposure to several environmental variables as well as our socio-economic and neighborhood settings, yet their interplay is not adequately analyzed and consequently not well understood. We aimed to build a machine learning (ML) pipeline, able to sufficiently identify the driving contextual factors for cardiovascular health. We conducted a comprehensive comparison of traditional regression approaches with multiple ML algorithms, from neighbor- to tree-based, boosting and deep learning methodologies, to predict cardiovascular mortality rates across Germany. We then extended our work on the investigation of the additional influence of individual risk factors for hypertension, an important risk factor for CVD morbidity and mortality, by using data from the largest epidemiological study in Germany, the German National Cohort. The results are presented in the attached slides.