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Salud, Ciencia y Tecnología Metaverse Basic and Applied Research Data & Metadata Interdisciplinary Rehabilitation Salud, Ciencia y Tecnología - Serie de Conferencias Community and Interculturality in Dialogue Seminars in Medical Writing and Education Health Leadership and Quality of Life Gamification and Augmented Reality Southern perspective / Perspectiva austral
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Original
Published: 2024-05-12
DOI: https://doi.org/10.56294/sctconf2024761

Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction

By
Ms. T. Sangeetha ,
Ms. T. Sangeetha

Research Scholar, Department of Computer Science, PSG College of Arts & Science, Coimbatore - 641014, Tamil Nadu, India

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Dr. K. Manikandan ,
Dr. K. Manikandan

Associate Professor, Department of Computer Science, PSG College of Arts & Science, Coimbatore - 641014, Tamil Nadu, India

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Dr. D. Victor Arokia Doss ,
Dr. D. Victor Arokia Doss

Associate Professor, Department of Biochemistry, PSG College of Arts & Science, Coimbatore - 641014, Tamil Nadu, India

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Abstract

Introduction: Heart Failure (HF) is a complicated condition as well as a significant public health issue. Data processing is now required for machine and statistical learning techniques while it helps to identify key features and eliminates unimportant, redundant, or noisy characteristics, hence minimizing the feature space's dimensions. A common cause of mortality in cases of heart disease is Dilated Cardiomyopathy (DCM).
Methods: The feature selection in this work depends on the Entropy Pelican Optimization Algorithm (EPOA). It is a recreation of pelicans' typical hunting behaviour. This is comparable to certain characteristics that lead to better approaches for solving high-dimensional datasets. Then Deep Autoencoder (DAE) classifier has been introduced for the prediction of patients. DAE classifier is employed to compute the system's nonlinear function through data from the normal and failure state.
Results: DAE was discovered to not only considerably increase accuracy but also to be beneficial when there is a limited amount of labelled data.Performance metrics like recall, precision, accuracy, f-measure, and error rate has been used for results analysis.
Conclusion: Publicly available benchmark dataset has been collected from Gene Expression Omnibus (GEO) repository to evaluate and contrast the suitability of the suggested classifier with other existing methods.

Keywords:

Heart failure (HF),
Dilated cardiomyopathy (DCM),
Entropy Pelican Optimization Algorithm (EPOA),
Gene Expression Omnibus (GEO),
Deep Autoencoder (DAE),
and FS (Feature Selection),

How to Cite

1.
Sangeetha MT, Manikandan DK, Arokia Doss DDV. Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction. Salud, Ciencia y Tecnología - Serie de Conferencias [Internet]. 2024 May 12 [cited 2024 Jul. 1];3:761. Available from: https://conferencias.saludcyt.ar/index.php/sctconf/article/view/761

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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Vol. 3 (2024)
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