Interpretability Analysis of Machine Learning Algorithms in the Detection of ST-Elevation Myocardial Infarction

Abstract

Recent studies suggested that ST-Elevation Myocardial Infarction (STEMI) can be detected in the ECG relying on machine learning (ML) algorithms. However, most of ML algorithms lack of an interpretability analysis, since they do not provide any justification for their decisions. In this study, we trained a Random Forest (RF) on the Physionet PTB database to automatically detect STEMI patients, considering 12-lead average templates as input. Then, we used the Local Interpretable Model-agnostic Explanations (LIME) method to highlight the input parts that mostly contributed to the detection. LIME interpretations were validated with the anatomical position of the myocardial infarction available within the dataset. Experimental results showed that RF achieved a high test set accuracy (ranging from 0.84 to 0.92). However, LIME identified areas within QRS complexes as the most relevant ones for the RF decision, rather than in the ST segment as expected. Our study suggests that, despite the test set accuracy, ML algorithms for STEMI classification, trained on small or unbalanced/biased populations, may rely on features which are not clinically significant. In this regard, interpretability algorithms like LIME may help in understanding possible pitfalls.