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Prediction of Heart Disease by Feature Selection Technique using Particle Swarm Optimization based on Logistic Regression2023 2nd International Conference on Futuristic Technologies (INCOFT)10.1109/INCOFT60753.2023.10425670
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Deep Neural Network With Feature Optimization Technique for Classification of Coronary Artery DiseaseHandbook of Research on Computer Vision and Image Processing in the Deep Learning Era10.4018/978-1-7998-8892-5.ch016

Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization

Pratibha Verma, Vineet Kumar Awasthi, Sanat Kumar Sahu, Akhilesh Kumar Shrivas

Source Title: International Journal of Applied Metaheuristic Computing (IJAMC)13(1)

ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781799885405|DOI: 10.4018/IJAMC.292504

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MLA

Verma, Pratibha, et al. "Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization." IJAMC vol.13, no.1 2022: pp.1-25. http://doi.org/10.4018/IJAMC.292504

APA

Verma, P., Awasthi, V. K., Sahu, S. K., & Shrivas, A. K. (2022). Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization. International Journal of Applied Metaheuristic Computing (IJAMC), 13(1), 1-25. http://doi.org/10.4018/IJAMC.292504

Chicago

Verma, Pratibha, et al. "Coronary Artery Disease Classification Using Deep Neural Network and Ensemble Models Optimized by Particle Swarm Optimization," International Journal of Applied Metaheuristic Computing (IJAMC) 13, no.1: 1-25. http://doi.org/10.4018/IJAMC.292504

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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.