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Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

his paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% wh...

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Main Authors: Krishnan, S., Magalingam, P., Ibrahim, R.
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science 2021
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/95039/1/PritheegaMagalingam2021_HybridDeepLearningModelUsingRecurrent.pdf
http://eprints.utm.my/id/eprint/95039/
http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476
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spelling my.utm.950392022-04-29T22:01:42Z http://eprints.utm.my/id/eprint/95039/ Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction Krishnan, S. Magalingam, P. Ibrahim, R. T Technology (General) his paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients. Institute of Advanced Engineering and Science 2021 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/95039/1/PritheegaMagalingam2021_HybridDeepLearningModelUsingRecurrent.pdf Krishnan, S. and Magalingam, P. and Ibrahim, R. (2021) Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction. International Journal of Electrical and Computer Engineering, 1 (6). ISSN 2088-8708 http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476 DOI: 10.11591/ijece.v11i6.pp5467-5476
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Krishnan, S.
Magalingam, P.
Ibrahim, R.
Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
description his paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.
format Article
author Krishnan, S.
Magalingam, P.
Ibrahim, R.
author_facet Krishnan, S.
Magalingam, P.
Ibrahim, R.
author_sort Krishnan, S.
title Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
title_short Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
title_full Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
title_fullStr Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
title_full_unstemmed Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
title_sort hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction
publisher Institute of Advanced Engineering and Science
publishDate 2021
url http://eprints.utm.my/id/eprint/95039/1/PritheegaMagalingam2021_HybridDeepLearningModelUsingRecurrent.pdf
http://eprints.utm.my/id/eprint/95039/
http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476
_version_ 1732945424713515008
score 13.211869

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