Machine learning models are increasingly used to estimate a person’s risk of cardiovascular disease, but bias can be introduced when health data is collected and prepared, affecting how well these models work for certain groups. This study used electronic health records from 109,490 people treated at Vanderbilt University Medical Center, in the US, to test whether several machine learning models performed equally well across race and gender. It also compared them with a widely used risk equation from the American Heart Association that does not use machine learning.
Most of the machine learning models were less biased than the American Heart Association equation. Differences between racial groups were small, but differences between women and men were considerably larger. According to the authors, both the risk equation and the machine learning models were biased against women.
The researchers also tested three ways of reducing this bias. Removing race and gender from the data did not significantly reduce bias in most models, and balancing the number of people from each group in the training data did not consistently help either. Balancing the proportion of people with cardiovascular disease in each group reduced the gender bias, but not the racial bias, and slightly reduced accuracy in many cases. The authors conclude that gender disparities in cardiovascular risk prediction need to be investigated and corrected.
Learn more about this study here: https://doi.org/10.1016/j.jbi.2023.104294
Reference:
Li, F., Wu, P., Ong, H.H., Peterson, J.F., Wei, W.-Q., & Zhao, J. (2023). Evaluating and mitigating bias in machine learning models for cardiovascular disease prediction. Journal of Biomedical Informatics, 138, 104294.