Cardiac abnormality prediction using tansig based multilayer perceptron
An artificial neural network (ANN) is a network designed with adaptation to a computer system. The developed computer system will perform functions oriented to the way the brain works (neuron concept). This study is an extension to the study of the suitability of ANN to be applied in numbers of ap...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit Universiti Kebangsaan Malaysia
2021
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| Online Access: | http://journalarticle.ukm.my/19102/ http://journalarticle.ukm.my/19102/1/22.pdf |
| _version_ | 1848814749822746624 |
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| author | Mohanty, Sibani Priyadarshini Syahrull Hi-Fi Syam Ahmad Jamil, Jailani Abdul Kadir, Mohd Salman Mohd Sabri, Fakroul Ridzuan Hashim, |
| author_facet | Mohanty, Sibani Priyadarshini Syahrull Hi-Fi Syam Ahmad Jamil, Jailani Abdul Kadir, Mohd Salman Mohd Sabri, Fakroul Ridzuan Hashim, |
| author_sort | Mohanty, Sibani Priyadarshini |
| building | UKM Institutional Repository |
| collection | Online Access |
| description | An artificial neural network (ANN) is a network designed with adaptation to a computer system. The developed computer
system will perform functions oriented to the way the brain works (neuron concept). This study is an extension to the
study of the suitability of ANN to be applied in numbers of applications, especially in the field of medical engineering.
ANN has been widely being used in medicine, ANN is widely applied in education, research, and even decision making.
In this study, ANN will be trained for pre-testing to predict the cardiac abnormalities symptom based on selected reference
parameters. This reference parameter is better known as the input parameter to the ANN to detect cardiac abnormalities,
among which are the of the height of peak/wave (amplitude) and time occurrence of peak/wave (duration of time)
extracted from the electrocardiogram (ECG) signal. A complete ECG complex contains a P peak, a QRS wave, and a T
peak. For each P peak, QRS wave, and T peak, amplitude height and duration will be measured to serve as input
parameters. This makes six parameters defined as inputs to the ANN. This study has used a Multilayer Perceptron (MLP)
network as ANN structure by being trained using three different training algorithms namely Backpropagation (BP),
Lavenberg Marquardt (LM) and Bayesian Regularization (BR). At the end of the study, it showed the MLP network which
by BR training algorithm gave the highest accuracy prediction (94.04%), followed by LM (92.95%) and BP (88.77%). In
this study all MLP networks were activated using the Tansig activation function. |
| first_indexed | 2025-11-15T00:39:02Z |
| format | Article |
| id | oai:generic.eprints.org:19102 |
| institution | Universiti Kebangasaan Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T00:39:02Z |
| publishDate | 2021 |
| publisher | Penerbit Universiti Kebangsaan Malaysia |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | oai:generic.eprints.org:191022022-07-26T07:42:57Z http://journalarticle.ukm.my/19102/ Cardiac abnormality prediction using tansig based multilayer perceptron Mohanty, Sibani Priyadarshini Syahrull Hi-Fi Syam Ahmad Jamil, Jailani Abdul Kadir, Mohd Salman Mohd Sabri, Fakroul Ridzuan Hashim, An artificial neural network (ANN) is a network designed with adaptation to a computer system. The developed computer system will perform functions oriented to the way the brain works (neuron concept). This study is an extension to the study of the suitability of ANN to be applied in numbers of applications, especially in the field of medical engineering. ANN has been widely being used in medicine, ANN is widely applied in education, research, and even decision making. In this study, ANN will be trained for pre-testing to predict the cardiac abnormalities symptom based on selected reference parameters. This reference parameter is better known as the input parameter to the ANN to detect cardiac abnormalities, among which are the of the height of peak/wave (amplitude) and time occurrence of peak/wave (duration of time) extracted from the electrocardiogram (ECG) signal. A complete ECG complex contains a P peak, a QRS wave, and a T peak. For each P peak, QRS wave, and T peak, amplitude height and duration will be measured to serve as input parameters. This makes six parameters defined as inputs to the ANN. This study has used a Multilayer Perceptron (MLP) network as ANN structure by being trained using three different training algorithms namely Backpropagation (BP), Lavenberg Marquardt (LM) and Bayesian Regularization (BR). At the end of the study, it showed the MLP network which by BR training algorithm gave the highest accuracy prediction (94.04%), followed by LM (92.95%) and BP (88.77%). In this study all MLP networks were activated using the Tansig activation function. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/19102/1/22.pdf Mohanty, Sibani Priyadarshini and Syahrull Hi-Fi Syam Ahmad Jamil, and Jailani Abdul Kadir, and Mohd Salman Mohd Sabri, and Fakroul Ridzuan Hashim, (2021) Cardiac abnormality prediction using tansig based multilayer perceptron. Jurnal Kejuruteraan, 4 (2(SI)). pp. 147-152. ISSN 0128-0198 https://www.ukm.my/jkukm/si-42-2021/ |
| spellingShingle | Mohanty, Sibani Priyadarshini Syahrull Hi-Fi Syam Ahmad Jamil, Jailani Abdul Kadir, Mohd Salman Mohd Sabri, Fakroul Ridzuan Hashim, Cardiac abnormality prediction using tansig based multilayer perceptron |
| title | Cardiac abnormality prediction using tansig based multilayer perceptron |
| title_full | Cardiac abnormality prediction using tansig based multilayer perceptron |
| title_fullStr | Cardiac abnormality prediction using tansig based multilayer perceptron |
| title_full_unstemmed | Cardiac abnormality prediction using tansig based multilayer perceptron |
| title_short | Cardiac abnormality prediction using tansig based multilayer perceptron |
| title_sort | cardiac abnormality prediction using tansig based multilayer perceptron |
| url | http://journalarticle.ukm.my/19102/ http://journalarticle.ukm.my/19102/ http://journalarticle.ukm.my/19102/1/22.pdf |