Machine learning for cardiovascular disease improves when social, environmental factors are included

Machine learning can accurately predict cardiovascular disease and guide treatment—but models that incorporate social determinants of health better capture risk and outcomes for diverse groups, finds a new study by researchers at New York University's School of Global Public Health and Tandon School of Engineering. The article, published in the American Journal of Preventive Medicine, also points to opportunities to improve how social and environmental variables are factored into machine learning algorithms.