Abstract
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.
Highlights
Coronary Artery Disease (CAD) is developed by the formation of plaques inside the walls of coronary arteries, resulting in the narrowing of lumens of coronary arteries (Pal & Chakraborty, 2011)
We describe the design of the experiments performed to evaluate the various Particle Swarm Optimization (PSO) models
We have proposed a new feature optimization technique namely PSO with learning classifier as ensemble based on voting technique and combination rule.The PSO has used the ensemble model as the learning classifier
Summary
Coronary Artery Disease (CAD) is developed by the formation of plaques inside the walls of coronary arteries, resulting in the narrowing of lumens of coronary arteries (Pal & Chakraborty, 2011). A variety of medical technology available in healthcare industries provide improved diagnosis of heart and coronary diseases. The healthcare industry nowadays produces a huge amount of unreadable and complex data related to patients, hospital resources, disease diagnosis, electronic patient records, medical devices, etc. This data is the main resource to be processed and analysed for knowledge extraction and used as guidance for decision-making and cost-savings (El-bialy, Salamay, Karam, & Khalifa, 2015)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
