scholarly journals Automated classification of coronary artery disease using discrete wavelet transform and back propagation neural network

2014 ◽  
Vol 9 (10) ◽  
pp. 440-451
Author(s):  
Sathish Kumar S. ◽  
Amutha R.
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Wavelet Transform ◽  
Coronary Artery ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Automated Classification ◽  
Artery Disease

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

scholarly journals Classification of coronary artery disease data sets by using a deep neural network

2017 ◽  
Vol 1 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Abdullah Caliskan ◽  
Mehmet Emin Yuksel
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Deep Neural Network ◽  
Cost Effective ◽  
Classification Performance ◽  
Data Sets ◽  
Neural Network Classifier ◽  
Artery Disease

Abstract In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

Transmission Line Using Discrete Wavelet Transform and Back-Propagation Neural Network Based on Clarke’s Transformation

2016 ◽  
Vol 818 ◽  
pp. 156-165 ◽  
Author(s):  
Makmur Saini ◽  
Abdullah Asuhaimi bin Mohd Zin ◽  
Mohd Wazir Bin Mustafa ◽  
Ahmad Rizal Sultan ◽  
Rahimuddin
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Transmission Line ◽  
Discrete Wavelet Transform ◽  
Back Propagation ◽  
Absolute Error ◽  
Back Propagation Neural Network ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Comparison Of The Results

This paper proposes a new technique of using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) based on Clarke’s transformation for fault classification and detection on a single circuit transmission line. Simulation and training process for the neural network are done by using PSCAD / EMTDC and MATLAB. Daubechies4 mother wavelet (DB4) is used to decompose the high frequency components of these signals. The wavelet transform coefficients (WTC) and wavelet energy coefficients (WEC) for classification fault and detect patterns used as input for neural network training back-propagation (BPNN). This information is then fed into a neural network to classify the fault condition. A DWT with quasi optimal performance for preprocessing stage are presented. This study also includes a comparison of the results of training BPPN and DWT with and without Clarke’s transformation, where the results show that using Clarke transformation in training will give in a smaller mean square error (MSE) and mean absolute error (MAE). The simulation also shows that the new algorithm is more reliable and accurate.

Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform

2013 ◽  
Vol 37 ◽  
pp. 274-282 ◽  
Author(s):  
Donna Giri ◽  
U. Rajendra Acharya ◽  
Roshan Joy Martis ◽  
S. Vinitha Sree ◽  
Teik-Cheng Lim ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Wavelet Transform ◽  
Coronary Artery ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Automated Diagnosis ◽  
Artery Disease

Automated classification of patients with coronary artery disease using grayscale features from left ventricle echocardiographic images

2013 ◽  
Vol 112 (3) ◽  
pp. 624-632 ◽  
Author(s):  
U. Rajendra Acharya ◽  
S. Vinitha Sree ◽  
M. Muthu Rama Krishnan ◽  
N. Krishnananda ◽  
Shetty Ranjan ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Automated Classification ◽  
Artery Disease ◽  
Echocardiographic Images
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