ECG Feature Importance Rankings: Cardiologists vs. Algorithms
This work addresses the validation of feature importance methods in medical diagnostics, providing insights for clinicians and researchers, but it is incremental as it applies existing methods to a new domain without major breakthroughs.
The study evaluated feature importance methods on real-world ECG data by comparing algorithm rankings to cardiologists' decision rules as ground truth, finding that some methods performed well overall while others varied across different pathologies.
Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. Some methods generally performed well and others performed poorly, while some methods did well on some but not all of the problems considered.