Understanding and Reducing Racial Bias in Cardiovascular Risk Prediction Using Novel AI Methods

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In this study, we will investigate the use of prediction scores and artificial intelligence models to predict heart disease based on a person's past medical history, demographic information, and electrocardiograms (simple recordings of the heart's activity). Although such risk scores are used abundantly in clinical practice, they are commonly developed from mostly white populations, and it is not well understood whether they work accurately for all UK ethnicities. To answer this, we will analyse how well existing scores work for different ethnicities and if they accurately predict heart disease for everyone. We will then use machine learning techniques to determine the most significant demographic and medical history factors that increase one's risk for heart disease in different ethnicities and create risk scores tailored to each ethnicity. Furthermore, to understand if similar ethnic bias exists in prediction models using artificial intelligence and electrocardiograms, we will compare models trained on data from mostly white populations versus models trained on data from more ethnically diverse populations. Finally, we will develop new artificial intelligence-based models that are more easily explainable than the current models, and that can predict heart disease more accurately and without ethnic bias.