Machine learning for cardiovascular disease improves when social, environmental factors are included, according to New York-based researchers.

A study at the New York University Tandon School of Engineering and the NYU School of Global Public Health emphasised the need for algorithms that incorporate community-level data and called for studies that include more diverse populations.

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. The research, 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.

Cardiovascular disease is responsible for nearly a third of all deaths worldwide and disproportionately affects lower socioeconomic groups. Increases in cardiovascular disease and deaths are attributed, in part, to social and environmental conditions – also known as social determinants of health – that influence diet and exercise.

“Cardiovascular disease is increasing, particularly in low- and middle-income countries and among communities of colour in places like the USA,” said Rumi Chunara, associate professor at NYU Tandon School of Engineering, the study’s senior author. “Because these changes are happening over such a short period of time, it is well known that our changing social and environmental factors, such as increased processed foods, are driving this change, as opposed to genetic factors which would change over much longer time scales.”

Machine learning – a type of artificial intelligence used to detect patterns in data – is being rapidly developed in cardiovascular research and care to predict disease risk, incidence and outcomes. Already, statistical methods are central in assessing cardiovascular disease risk and US prevention guidelines. Developing predictive models gives health professionals actionable information by quantifying a patient’s risk and guiding the prescription of drugs or other preventive measures.

Cardiovascular disease risk is typically computed using clinical information, such as blood pressure and cholesterol levels, but rarely takes social determinants, such as neighbourhood-level factors, into account. Chunara and her colleagues sought to understand how social and environmental factors were beginning to be integrated into machine-learning algorithms for cardiovascular disease – what factors are considered, how they are being analysed, and what methods improve these models.

“Social and environmental factors have complex, non-linear interactions with cardiovascular disease,” said Chunara. “Machine learning can be particularly useful in capturing these intricate relationships.”

The researchers analysed existing research on machine learning and cardiovascular disease risk, screening more than 1600 articles and focusing on 48 peer-reviewed studies published in journals between 1995 and 2020.

They found that including social determinants of health in machine-learning models improved the ability to predict cardiovascular outcomes such as rehospitalisation, heart failure and stroke. However, these models did not typically include the full list of community-level or environmental variables that were important in cardiovascular disease risk. Some studies did include additional factors such as income, marital status, social isolation, pollution and health insurance, but only five studies considered environmental factors such as the walkability of a community and the availability of resources such as grocery stores.

The researchers also noted the lack of geographic diversity in the studies, as the majority used data from the USA, Europe and China, neglecting many parts of the world experiencing increases in cardiovascular disease.

“If you only do research in places like the USA or Europe, you’ll miss how social determinants and other environmental factors related to cardiovascular risk interact in different settings and the knowledge generated will be limited,” said Chunara.

Stephanie Cook, assistant professor of biostatistics at NYU School of Global Public Health and a study author, added: “Our study shows there is room to more systematically and comprehensively incorporate social determinants of health into cardiovascular disease statistical risk prediction models. In recent years, there has been a growing emphasis on capturing data on social determinants of health – such as employment, education, food and social support – in electronic health records, which creates an opportunity to use these variables in machine-learning studies and further improve the performance of risk prediction, particularly for vulnerable groups.”

And Chunara said: “Including social determinants of health in machine-learning models can help us to disentangle where disparities are rooted and bring attention to where in the risk structure we should intervene. For example, it can improve clinical practice by helping health professionals identify patients in need of referral to community resources like housing services and broadly reinforces the intricate synergy between the health of individuals and our environmental resources.”

In addition to Chunara and Cook, study authors included Yuan Zhao, Erica Wood and Nicholas Mirin, students at the NYU School of Global Public Health. The research was supported by funding from the National Science Foundation.