Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network
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 bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/22/6477 |
| _version_ | 1827702339952181248 |
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| author | Paulo Vitor de Campos Souza Edwin Lughofer |
| author_facet | Paulo Vitor de Campos Souza Edwin Lughofer |
| author_sort | Paulo Vitor de Campos Souza |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:53:59Z |
| format | Article |
| id | doaj.art-85db1f6d64224aa9947b29a3edaa1fe5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:53:59Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-85db1f6d64224aa9947b29a3edaa1fe52023-11-20T20:47:19ZengMDPI AGSensors1424-82202020-11-012022647710.3390/s20226477Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural NetworkPaulo Vitor de Campos Souza0Edwin Lughofer1Department of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz Altenberger Strasse 69, 4040 Linz, AustriaDepartment of Knowledge-Based Mathematical Systems, Johannes Kepler University Linz Altenberger Strasse 69, 4040 Linz, AustriaHeart 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.https://www.mdpi.com/1424-8220/20/22/6477evolving fuzzy neural networkheart murmurSOFpattern classification problem |
| spellingShingle | Paulo Vitor de Campos Souza Edwin Lughofer Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network Sensors evolving fuzzy neural network heart murmur SOF pattern classification problem |
| title | Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network |
| title_full | Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network |
| title_fullStr | Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network |
| title_full_unstemmed | Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network |
| title_short | Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network |
| title_sort | identification of heart sounds with an interpretable evolving fuzzy neural network |
| topic | evolving fuzzy neural network heart murmur SOF pattern classification problem |
| url | https://www.mdpi.com/1424-8220/20/22/6477 |
| work_keys_str_mv | AT paulovitordecampossouza identificationofheartsoundswithaninterpretableevolvingfuzzyneuralnetwork AT edwinlughofer identificationofheartsoundswithaninterpretableevolvingfuzzyneuralnetwork |