2023/04/05 by Temesgen Mehari, Mehari, Temesgen, Ashish Sundar +17 · 1 citation
Computer Science · Engineering · Medicine · #ECG Monitoring and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Fault Detection and Control Systems #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Medical Physics (physics.med-ph) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2304.02577
openalex publication_date 2023/04/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.