Automatic Heart Disease Diagnosis Based on MRI Image Using Deep Neural Network: Adaptive Bacterial Foraging Optimization Algorithm-Based Feature Selection

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Author(s): Manaswini Pradhan (Fakir Mohan University, India)and Alauddin Bhuiyan (Icahn School of Medicine at Mount Sinai, USA)
Copyright: 2023
Pages: 22
Source title: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
Source Author(s)/Editor(s): Thomas M. Connolly (DS Partnership, UK), Petros Papadopoulos (University of Strathclyde, UK)and Mario Soflano (Glasgow Caledonian University, UK)
DOI: 10.4018/978-1-6684-5092-5.ch010

Keywords: Business & Management / Decision Support Systems / Health Information Systems / Medical Information Science Reference

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Abstract

In this chapter, the authors propose an adaptive bacterial foraging optimization (ABFO) algorithm for selection of features to increase the classification accuracy of heart disease diagnosis. In this approach, noises contained in the cardiac image are removed using median filter initially. Then, GLCM features are extracted from the cardiac image. Among the extracted features, optimal features are chosen using the ABFO algorithm. These selected features are then input to the classifier, which is a support vector neural network (RBNN). The classifier classifies the image into normal and abnormal. Simulation results show that the ABFO-based RBNN performs better than the conventional RBNN, ANN, KNN, and SVM in terms of accuracy.

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Automatic Heart Disease Diagnosis Based on MRI Image Using Deep Neural Network: Adaptive Bacterial Foraging Optimization Algorithm-Based Feature Selection

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