Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease

2019 ◽  
Vol 38 (1) ◽  
Author(s):  
Mariam Zomorodi‐moghadam ◽  
Moloud Abdar ◽  
Zohreh Davarzani ◽  
Xujuan Zhou ◽  
Pawel Pławiak ◽  
...  
2022 ◽  
Vol 13 (1) ◽  
pp. 0-0

Nowadays, many people are suffering from several health related issues in which Coronary Artery Disease (CAD) is an important one. Identification, prevention and diagnosis of diseases is a very challenging task in the field of medical science. This paper proposes a new feature optimization technique known as PSO-Ensemble1 to reduce the number of features from CAD datasets. The proposed model is based on Particle Swarm Optimization (PSO) with Ensemble1 classifier as the objective function and is compared with other optimization techniques like PSO-CFSE and PSO-J48 with two benchmark CAD datasets. The main objective of this research work is to classify CAD with the proposed PSO-Ensemble1 model using the Ensemble Technique.


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