Universiti Teknologi Malaysia Institutional Repository

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

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


Official URL: http://dx.doi.org/10.11591/ijece.v11i6.pp5467-5476


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.

Item Type:Article
Uncontrolled Keywords:deep neural network, gated recurrent unit, long short term memory
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:95039
Deposited By: Narimah Nawil
Deposited On:30 Apr 2022 06:01
Last Modified:30 Apr 2022 06:01

Repository Staff Only: item control page