Motality prediction of ICU rheumatic heart disease with imbalanced data based on machine learning

Abstract

<p>Linked to poverty, rheumatic heart disease (RHD) disproportionately burdens the developing world, receiving less attention than other infectious diseases. Resampling and cost-sensitive learning techniques are applied to predict the mortality risk of imbalanced RHD datasets. A total of 57 models were constructed, and was comprised of 50 resampled machine learning (ML) models and 7 cost-sensitive learning models. The results from the Friedman and Nemenyi tests highlight the superior performance of the cost-sensitive support vector classification model, with an AUC of 0.888, sensitivity of 0.800, G-means of 0.806, and a Brier score of 0.061. The global and local interpretability are advanced through two post-hoc interpretable ML methods, facilitating the prioritization of key features associated with mortality risk, the determination of thresholds for features, and a comprehension of how variations in these features influence patient mortality rates. These findings may prove to be clinically valuable, assisting clinicians in tailoring precise management that is essential to maximize the survival of RHD patients.</p>