Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network
Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz Altenberger Strasse 69, 4040 Linz, Austria
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These authors contributed equally to this work.
Sensors 2020, 20(22), 6477; https://doi.org/10.3390/s20226477
Received: 20 October 2020
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Revised: 9 November 2020
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Accepted: 10 November 2020
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Published: 12 November 2020
(This article belongs to the Special Issue Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing)
Heart problems are responsible for the majority of deaths worldwide. The use of intelligent techniques to assist in the identification of existing patterns in these diseases can facilitate treatments and decision making in the field of medicine. This work aims to extract knowledge from a dataset based on heart noise behaviors in order to determine whether heart murmur predilection exists or not in the analyzed patients. A heart murmur can be pathological due to defects in the heart, so the use of an evolving hybrid technique can assist in detecting this comorbidity team, and at the same time, extract knowledge through fuzzy linguistic rules, facilitating the understanding of the nature of the evaluated data. Heart disease detection tests were performed to compare the proposed hybrid model’s performance with state of the art for the subject. The results obtained (90.75% accuracy) prove that in addition to great assertiveness in detecting heart murmurs, the evolving hybrid model could be concomitant with the extraction of knowledge from data submitted to an intelligent approach.
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MDPI and ACS Style
de Campos Souza, P.V.; Lughofer, E. Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network. Sensors 2020, 20, 6477. https://doi.org/10.3390/s20226477
AMA Style
de Campos Souza PV, Lughofer E. Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network. Sensors. 2020; 20(22):6477. https://doi.org/10.3390/s20226477
Chicago/Turabian Stylede Campos Souza, Paulo V., and Edwin Lughofer. 2020. "Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network" Sensors 20, no. 22: 6477. https://doi.org/10.3390/s20226477
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