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Deep Neural Network With Feature Optimization Technique for Classification of Coronary Artery Disease
Abstract
Coronary artery disease (CAD) is of significant concern among the population worldwide. The deep neural network (DNN) methods co-operate and play a crucial role in identifying diseases in CAD. The classification techniques like deep neural network (DNN) and enhanced deep neural network (EDNN) model are best suited for problem solving. A model is robust with the integration of feature selection technique (FST) like genetic algorithm (GA) and particle swarm optimization (PSO). This research proposes an integrated model of GA, PSO, and DNN for classification of CAD. The E-DNN model with a subset feature of CAD datasets gives enhanced results as compared to the DNN model. The E-DNN model gives a more correct and precise classification performance.
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