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Published in: New Generation Computing 4/2023

04-11-2023

A Dense Network Approach with Gaussian Optimizer for Cardiovascular Disease Prediction

Authors: A. Saran Kumar, R. Rekha

Published in: New Generation Computing | Issue 4/2023

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Abstract

The effective method for cardiovascular disease (CVD) risk prediction is done by training the deep neural networks on the well-defined training dataset. The irregular subset from the real dataset with a greater data variance is considered for prediction. The proposed system uses the trained datasets to separate common and greatly biased subsets for accurately implementing the prediction models when many previous models are learning from the random samples of training datasets. The feature selection is done with a Binary Krill Herd meta-heuristic optimizer (B-KHA), and the extracted features are fed to the CapNet model for prediction purposes. In addition, the isolated training groups learn the network classifiers. This proposed model used the Cleveland dataset gathered from online resources. The experiment proves that the proposed model improves the network performance by appropriate prediction. The suggested model shows that the experimental outcomes perform better than the traditional machine learning algorithms, with 95% accuracy, 94% specificity, 94% precision, 97% sensitivity, 95% F1-score, and 90% Mathews’ Correlation Coefficient (MCC).

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Metadata
Title
A Dense Network Approach with Gaussian Optimizer for Cardiovascular Disease Prediction
Authors
A. Saran Kumar
R. Rekha
Publication date
04-11-2023
Publisher
Springer Japan
Published in
New Generation Computing / Issue 4/2023
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-023-00234-1

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