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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. <\/jats:p>","DOI":"10.3233\/ica-230719","type":"journal-article","created":{"date-parts":[[2023,9,5]],"date-time":"2023-09-05T15:47:22Z","timestamp":1693928842000},"page":"43-58","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["An explainable machine learning system for left bundle branch block detection and classification"],"prefix":"10.1177","volume":"31","author":[{"given":"Beatriz","family":"Macas","sequence":"first","affiliation":[{"name":"Instituto de Ingenier\u00eda Biom\u00e9dica, Fac. de Ingenier\u00eda, Universidad de Buenos Aires (FIUBA), Argentina"},{"name":"Instituto Argentino de Matem\u00e1tica Alberto P. 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