Diagnosing the Heart Diseases through Recurrent Neural Network in Associates with Artificial Fish Swarm Optimization
Keywords:
Heart diseases, Artificial Intelligence, Artificial Fish Swarm Optimization (AFSO), Recurrent Neural NetworksAbstract
The main objective of this study is to use Artificial Intelligence (AI) technique to diagnose normal and abnormal cardiac disease situations. This research considers couple of benchmark database Cleveland and Hungarain from UCI data repository. This research considers RNN (Recurrent Neural Network) – A LSTM (Long Short-Term Memory) technique diagnoses heart diseases effectively. It is apparent from the investigation that tuning weights play a vital role is enhancing the output performance. Identifying the optimal weights through manual consumes more time and complex is process. The research involves optimization techniques to resolve the above issue. The optimization technique involve during process are PSO (Particle Swarm Optimization) and AFSO (Artificial Fish Swarm Optimization). The results shows that optimization involves in this process enhance the process effectively over traditional approach. AFSO associate RNN-LSTM achieves 98.34% accuracy that is better over comparative techniques.
Downloads
References
P. Priyanga, Veena V. Pattankar and S. Sridevi,"A hybrid recurrent neural network-logisticchaos-based whale optimization framework for heart disease prediction with electronic health records", Computational Intelligence, Vol.37, pp.315-343, 2021.
Johanna Ralston, K. Srinath Reddy, Valentin Fuster and JagatNarula,"Cardiovascular Diseases on the Global Agenda: The United Nations High Level Meeting, Sustainable Development Goals, and the Way Forward",Global Heart, Vol.11, No.4, pp.375-379, 2016.
Omer Deperlioglu, UtkuKose, Deepak Gupta, Ashish Khanna and Arun Kumar Sangaiah,"Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network",Computer Communications, Vol.162, pp.31-50, 2020.
Saïd Bichali, David Malorey, Nadir Benbrik, Laurianne Le Gloan, Christèle Gras-Le Guen, Alban-ElouenBaruteau and Elise Launay,"Measurement, consequences and determinants of time to diagnosis inchildren with new-onset heart failure: A population-based retrospectivestudy (DIACARD study)",International Journal of Cardiology, Vol.318, pp.87-93, 2020.
Philipp Sodmann, Marcus Vollmer, NeetikaNath and Lars Kaderali,"A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms"Physiological Measurement,Vol.39, pp.1-21, 2018.
Pengyi Hao, Xiang Gao, Zhihe Li, Jinglin Zhang, Fuli Wu and Cong Bai,"Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images",Computer Methods and Programs in Biomedicine, Vol.184, pp.1-11, 2019.
Georgios Petmezas, Kostas Haris, Leandros Stefanopoulos, Vassilis Kilintzis, Andreas Tzavelis, John A Rogers, Aggelos KKatsaggelos and Nicos Maglaveras,"Automated Atrial Fibrillation Detection using a Hybrid CNN-LSTM Networkon Imbalanced ECG Datasets",Biomedical Signal Processing and Control, Vol.63, pp.1-9, 2021.
U. Rajendra Acharya, ShuLih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, Arkadiusz Gertych and RuSanTan,"A deep convolutional neural network model to classify heartbeats",Computers in Biology and Medicine, Vol.89, pp.389-396, 2017.
SerkanKiranyaz, TurkerInce and MoncefGabbouj,"Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks", IEEE Transactions on Biomedical Engineering, Vol.63, No.3, pp.664-675, 2016.
ShuLih Oh, Eddie Y.K.Ng, Ru San Tan and U. Rajendra Acharya, "Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats",Computers in Biology and Medicine, Vol.102, pp.278-287, 2018.
SajadMousavi and FatemehAfghah,"Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach",IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1308-1312, 2019.
Lahcen El bouny, Mohammed Khalil and AbdellahAdib,"An End-to-End Multi-Level Wavelet Convolutional Neural Networksfor heart diseases diagnosis",Neurocomputing, Vol.417, pp.187-201, 2020.
Ming Liu and Younghoon Kim,"Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory", Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.2707-2710, 2018.
Zhenfei Wang, Jinlei Chen, Zhiyu Zheng and Junfeng Wang, "Sample expansion-oriented LDBN heart disease risk forecast model", International Wireless Communications & Mobile Computing Conference (IWCMC), pp.1327-1332, 2018.
MOHAMMAD AYOUB KHAN, "AnIoT Framework for Heart Disease Prediction Based on MDCNN Classifier", IEEE Access, Vol.8, pp.34717-34727, 2020.
BhargavaK Reddy and Dursun Delen,"Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology", Computers in Biology and Medicine, Vol.101, pp.199-209, 2018.
Mariam Zomorodi- moghadam, "Hybrid particle swarm optimization for rule discovery in the diagnosis of coronary artery disease", Expert Systems, Vol.38, pp.1-17, 2019.
A.M. Barani, R.Latha and R.Manikandan,"Implementation of Artificial Fish SwarmOptimization for Cardiovascular Heart Disease",International Journal of Recent Technology and Engineering, Vol.8, pp.134-136, 2019.
Jalil Nourmohammadi- Khiarak, Mohammad-Reza Feizi-Derakhshi, KhadijehBehrouzi, SamanehMazaheri, YasharZamani-Harghalani and Rohollah Moosavi Tayebi, "New hybrid method for heart disease diagnosis utilizing optimizational algorithm in feature selection", Health and Technology, Vol.10, pp.667-678, 2020.
Annisa Darmawahyuni, SitiNurmaini, Meiryka Yuwandini, Muhammad NaufalRachmatullah, FirdausFirdaus and BambangTutuko, "Congestive heart failure waveform classification based on short time-step analysis with recurrent network", Informatics in Medicine Unlocked, Vol.21, pp.1-10, 2020.
Zahra Ebrahimi, Mohammad Loni, Masoud Daneshtalab and Arash Gharehbaghi, "A Review on Deep Learning Methods for ECG Arrhythmia Classification", Expert Systems with Applications, Vol.X, pp.1-40, 2020.
A.M.Barani and R.Latha,"A Smart Prediction Tool for Cardiovascular Heart Diseases Using AFSO Algorithm", International Journal of Grid and Distributed Computing, Vol.13, No.2, pp.648-656, 2020.
RenjiP. Cherian, Noby Thomas and Sunder Venkitachalam, "Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm", Journal of Biomedical Informatics, Vol.110, pp.103543, 2020.
Haotian Shi, Chengjin Qin, Dengyu Xiao, Liqun Zhao and Chengliang Liu, "Automated heartbeat classification based on deep neural network with multiple input layers",Knowledge-Based Systems, pp.1-8, 2019.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
