ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Medical Physics
2023-04-06 v1 Machine Learning
Signal Processing
Abstract
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.
Cite
@article{arxiv.2304.02577,
title = {ECG Feature Importance Rankings: Cardiologists vs. Algorithms},
author = {Temesgen Mehari and Ashish Sundar and Alen Bosnjakovic and Peter Harris and Steven E. Williams and Axel Loewe and Olaf Doessel and Claudia Nagel and Nils Strodthoff and Philip J. Aston},
journal= {arXiv preprint arXiv:2304.02577},
year = {2023}
}