Fairness-Aware Machine Learning for Predictive Risk Scoring in Cardiovascular Disease
Amenah Jaafar Saeed
Al-Mustansiriya University / Department of Administrative and Financial Affairs / Administrative Affairs / Personnel Affairs Division, Baghdad, Iraq.
Maha Jaafar Saeed
Al-Mustansiriya University / Citizens' Affairs Division, Baghdad, Iraq.
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http://doi.org/10.37648/ijiest.v12i01.011
Abstract
Cardiovascular disease (CVD) is the most prevalent cause of mortality throughout the world. The prediction of risk factors for CVD helps improve prevention techniques. In the past several years, one of the most promising avenues of research has been the application of Machine Learning (ML) techniques for the prediction of CVD risk factors. Telomere length is one of many types of complex data that has been shown to benefit from the predictive ability of ML when compared to more traditional CVD risk factor prediction methodologies. One of the drawbacks of predictive algorithms in risk assessment is the perpetuation of the existing biases that are in the data used for the training of the algorithms. In training data, the perpetuation of age, race and gender bias in the data will result in inequitable health outcomes for the different subpopulations. This study addresses this issue by providing a ML based CVD prediction framework that is equity-based. Fairness in ML is accomplished by employing a concept known as fair regularization in the training of the algorithms, which provides equivocal, interpretable risk assessment scores. Our framework was compared against a baseline theoretical model and several competing ML models that utilized CVD data contained in an electronic health record (EHR) database. Results indicated that baseline CVD prediction models showed the greatest performance in prediction of CVD risk, but at the greatest inequity. In contrast to the baseline theoretical model, our prediction model demonstrated the greatest performance of a prediction model that is both fair and equity-based. Results from our study showed the integration of fairness and predictability is not only achievable, but is necessary when developing healthcare based ML frameworks for CVD risk prediction models.
Keywords: Fairness-aware machine learning; Cardiovascular disease; Risk prediction; Algorithmic bias; Clinical decision support; Health equity
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