Social factors in machine learning models aid CVD prediction
Rumi Chunara, professor of computer science and engineering at NYU Tandon, and a member of the faculty of the NYU School of Global Public Health co-authored this research.
Machine learning approaches are useful for cardiovascular disease prediction, and model performance is improved with inclusion of social determinants of health, according to a review published online July 27 in the American Journal of Preventive Medicine.
The study constitutes a systematic review of articles on the use of machine learning algorithms for cardiovascular disease prediction that incorporated social determinants of health. The team reviewed research from North America, Europe, and China. Gender, race/ethnicity, marital status, occupation, and income were the most frequently included social determinants of health from those areas.
A focus on social factors could better help medical professionals connect with their patients, and improve medical practices by incorporating an aspect of community, reinforcing the intricate synergy between the health of individuals and environmental resources.