TY - GEN
T1 - Evolving fuzzy neural network based on null-unineurons for the identification of coronary artery disease
AU - Guimaraes, Augusto Junio
AU - De Campos Souza, Paulo Vitor
AU - Batista, Huoston Rodrigues
AU - Lughofer, Edwin
N1 - Funding Information:
The authors acknowledge the support by the Austrian Science Fund (FWF): contract number P32272-N38, acronym IL-EFS.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Coronary diseases affect a large part of the world population and have become the target of significant research in the academic field. The creation and use of intelligent models to facilitate the diagnosis of these diseases can allow treatments to be performed promptly to avoid further problems for patients. This paper applies an innovative evolving fuzzy neural network model to solve the problem of coronary heart disease diagnosis and extract valuable insights from the evaluated dataset. The null-unineurons that compose the model's architecture can extract fuzzy rules, representing linguistic knowledge about the target problem. A dataset that condenses the most famous data sources on this problem to classify coronary heart disease was applied to state-of-the-art models of evolving fuzzy systems. The results obtained by the model applied in this study are similar to the state-of-the-art results. Furthermore, the model provides relevant interpretations about the evolution of the problem evaluation.
AB - Coronary diseases affect a large part of the world population and have become the target of significant research in the academic field. The creation and use of intelligent models to facilitate the diagnosis of these diseases can allow treatments to be performed promptly to avoid further problems for patients. This paper applies an innovative evolving fuzzy neural network model to solve the problem of coronary heart disease diagnosis and extract valuable insights from the evaluated dataset. The null-unineurons that compose the model's architecture can extract fuzzy rules, representing linguistic knowledge about the target problem. A dataset that condenses the most famous data sources on this problem to classify coronary heart disease was applied to state-of-the-art models of evolving fuzzy systems. The results obtained by the model applied in this study are similar to the state-of-the-art results. Furthermore, the model provides relevant interpretations about the evolution of the problem evaluation.
KW - coronary artery disease diagnosis
KW - Evolving Fuzzy Neural Networks
KW - null-unineurons
KW - pattern classification
UR - http://www.scopus.com/inward/record.url?scp=85142700222&partnerID=8YFLogxK
U2 - 10.1109/SMC53654.2022.9945296
DO - 10.1109/SMC53654.2022.9945296
M3 - Conference contribution
AN - SCOPUS:85142700222
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 2681
EP - 2688
BT - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022
Y2 - 9 October 2022 through 12 October 2022
ER -
