Accurate Bayesian prediction of cardiovascular-related mortality using ambulatory blood pressure measurements

Abstract

Hypertension is the leading cause of cardiovascular-related mortality (CVRM), affecting approximately 1 billion people worldwide. To enable patients at significant risk of CVRM to be treated appropriately, it is essential to correctly diagnose hypertensive patients at an early stage. Our work achieves highly accurate risk scores and classification using 24-h Ambulatory Blood Pressure Monitoring (ABPM) to improve predictions. It involves two stages: (1) time series feature extraction using sliding window clustering techniques and transformations on raw ABPM signals, and (2) incorporation of these features and patient attributes into a probabilistic classifier to predict whether patients will die from cardiovascular-related illness within a median period of 8 years. When applied to a cohort of 5644 hypertensive patients, with 20% held out for testing, a K2 Bayesian network classifier (BNC) achieves 89.67% test accuracy on the final evaluation. We evaluate various BNC approaches with and without ABPM features, concluding that best performance arises from combining APBM features and clinical features in a BNC that represents multiple interactions, learned with some human knowledge in the form of arc constraints.