An explainable machine learning system using a reduced set of biologically inspired ECG features improved LBBB classification performance compared to previous models on the same dataset.
Does an explainable machine learning system using biologically inspired ECG features improve LBBB classification performance?
An explainable machine learning approach using biologically inspired ECG features improves LBBB classification and offers interpretable diagnostic support for clinicians.
Left bundle branch block is a cardiac conduction disorder that occurs when the electrical impulses that control the heartbeat are blocked or delayed as they travel through the left bundle branch of the cardiac conduction system providing a characteristic electrocardiogram (ECG) pattern. A reduced set of biologically inspired features extracted from ECG data is proposed and used to train a variety of machine learning models for the LBBB classification task. Then, different methods are used to evaluate the importance of the features in the classification process of each model and to further reduce the feature set while maintaining the classification performance. The performances obtained by the models using different metrics improve those obtained by other authors in the literature on the same dataset. Finally, XAI techniques are used to verify that the predictions made by the models are consistent with the existing relationships between the data. This increases the reliability of the models and their usefulness in the diagnostic support process. These explanations can help clinicians to better understand the reasoning behind diagnostic decisions.
Ordoñez et al. (Wed,) conducted a other in Left bundle branch block. Explainable machine learning system using biologically inspired ECG features vs. Other models in the literature was evaluated on LBBB classification performance. An explainable machine learning system using a reduced set of biologically inspired ECG features improved LBBB classification performance compared to previous models on the same dataset.