Sangeetha, T and Manikandan, K and Victor Arokia Doss, D (2024) Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction. Entropy pelican optimization algorithm (epoa) based feature selection and deep autoencoder (dae) of heart failure status prediction, 3. ISSN 2953-4860

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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.

Item Type: Article
Uncontrolled Keywords: Heart Failure (HF); Dilated Cardiomyopathy (DCM); Entropy Pelican Optimization Algorithm (EPOA); Gene Expression Omnibus (GEO); Deep Autoencoder (DAE); FS (Feature Selection)
Divisions: PSG College of Arts and Science > Department of Computer Science
Depositing User: Mr Team Mosys
Date Deposited: 05 Oct 2024 05:02
Last Modified: 05 Oct 2024 05:02
URI: https://ir.psgcas.ac.in/id/eprint/2309

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