ArticleSensors (Basel, Switzerland)2020
Identification of Heart Sounds with an Interpretable Evolving Fuzzy Neural Network.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Neuro-fuzzy systems in internet of medical things: a systematic review on applications, taxonomy, challenges and open issues.Frontiers in digital health · 2026Review
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- LightFD: Real-Time Fault Diagnosis with Edge Intelligence for Power Transformers.Sensors (Basel, Switzerland) · 2022Article
- Modern Trends and Applications of Intelligent Methods in Biomedical Signal and Image Processing.Sensors (Basel, Switzerland) · 2021Article
- Analysis of Sports Injury Estimation Model Based on Mutation Fuzzy Neural Network.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.