Interpretable estimation of the risk of heart failure hospitalization from a 30-second electrocardiogram

TL;DR

This paper demonstrates that machine learning models can accurately predict heart failure hospitalization risk from a 30-second ECG while providing interpretable insights into the contributing factors.

Contribution

It introduces an explainable machine learning approach using XGBoost and SHAP values for risk estimation from ECG data, improving prediction accuracy and interpretability.

Findings

Achieved a concordance index of 0.828 for risk prediction.

Area under the curve was 0.853 at one year and 0.858 at two years.

Model enables rapid, interpretable risk assessment from a short ECG test.

Abstract

Survival modeling in healthcare relies on explainable statistical models; yet, their underlying assumptions are often simplistic and, thus, unrealistic. Machine learning models can estimate more complex relationships and lead to more accurate predictions, but are non-interpretable. This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations. We train an eXtreme Gradient Boosting accelerated failure time model and exploit SHapley Additive exPlanations values to explain the effect of each feature on predictions. Our model achieved a concordance index of 0.828 and an area under the curve of 0.853 at one year and 0.858 at two years on a held-out test set of 6,573 patients. These results show that a rapid test based on an electrocardiogram could be crucial in targeting and treating high-risk individuals.