Cardiac abnormality prediction using tansig based multilayer perceptron

• Main Authors: Mohanty, Sibani Priyadarshini, Syahrull Hi-Fi Syam Ahmad Jamil,, Jailani Abdul Kadir,, Mohd Salman Mohd Sabri,, Fakroul Ridzuan Hashim,
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2021
• Online Access: http://journalarticle.ukm.my/19102/1/22.pdf; http://journalarticle.ukm.my/19102/; https://www.ukm.my/jkukm/si-42-2021/

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.