Hybrid pso-rbfnn and proposed algorithms of DDDWT for the heart disease classification
Author
Source
Engineering and Technology Journal
Issue
Vol. 39, Issue 4A (30 Apr. 2021), pp.520-527, 8 p.
Publisher
Publication Date
2021-04-30
Country of Publication
Iraq
No. of Pages
8
Main Subjects
Topics
Abstract EN
This paper introduced a Particle Swarm Optimization-Radial Basis Function Neural Networks (PSO-RBFNN)-based system for heart disease detection that used the PSO algorithm to optimize RBFNN parameters.
The newly developed signal digital algorithm presents the results of a new image contrast enhancement approach using Double Density Discrete Wavelet transform DDDWT for extraction of features, using adaptive DDDWT for the elimination of noise, and the use of PSO and ANN methods to classify the output from the Electrocardiogram (EGGS).
It also provides identification of all techniques and MATLAB codes used to improve the processes.
This approach merged the global search power of the PSO algorithm with the high efficiency of RBFNN's local optimums, overcome the inconsistency of the PSO algorithm and the RBFNN downside, quickly leading to a local minimum.
The results show that, as compared to other approaches, the PSO-RBFNN model of heart disease diagnosis is highly accurate in detecting and predicting.
American Psychological Association (APA)
Jabbar, Ansam S.. 2021. Hybrid pso-rbfnn and proposed algorithms of DDDWT for the heart disease classification. Engineering and Technology Journal،Vol. 39, no. 4A, pp.520-527.
https://search.emarefa.net/detail/BIM-1281562
Modern Language Association (MLA)
Jabbar, Ansam S.. Hybrid pso-rbfnn and proposed algorithms of DDDWT for the heart disease classification. Engineering and Technology Journal Vol. 39, no. 4A (2021), pp.520-527.
https://search.emarefa.net/detail/BIM-1281562
American Medical Association (AMA)
Jabbar, Ansam S.. Hybrid pso-rbfnn and proposed algorithms of DDDWT for the heart disease classification. Engineering and Technology Journal. 2021. Vol. 39, no. 4A, pp.520-527.
https://search.emarefa.net/detail/BIM-1281562
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 526-527
Record ID
BIM-1281562