scholarly journals GW29-e0534 Coronary artery disease in patients with abnormal myocardial perfusion can be predicted using artificial neural network

2018 ◽  
Vol 72 (16) ◽  
pp. C91
Author(s):  
Elena Yaroslavskaya ◽  
Vadim Kuznetsov ◽  
Dmitriy Krinochkin ◽  
Dmitriy Teffenberg ◽  
Elena Gorbatenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Artificial Neural ◽  
Artery Disease

Artificial neural network models for coronary artery disease

2020 ◽  
Vol 15 ◽  
Author(s):  
Elham Shamsara ◽  
Sara Saffar Soflaei ◽  
Mohammad Tajfard ◽  
Ivan Yamshchikov ◽  
Habibollah Esmaili ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Diagnostic Model ◽  
Early Prediction ◽  
Artificial Neural ◽  
Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among different machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Different methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant difference among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among different methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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.

Stress-induced Myocardial Ischaemia – Perfusion Contrast Echocardiography to Evaluate Presence and Severity of Coronary Artery Disease

2011 ◽  
Vol 7 (3) ◽  
pp. 172
Author(s):  
Benoy Nalin Shah ◽  
Roxy Senior ◽  
◽  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Myocardial Perfusion ◽  
Contrast Echocardiography ◽  
Evidence Base ◽  
Suspected Coronary Artery Disease ◽  
Ultrasound Contrast Agents ◽  
Prognostic Information ◽  
Non Invasive ◽  
Artery Disease

The development of stable transpulmonary ultrasound contrast agents (UCAs) has allowed the echocardiographic assessment of myocardial perfusion, a technique known as myocardial contrast echocardiography (MCE). MCE exploits the ultrasonic properties of UCAs, which consist of acoustically active gas-filled microspheres. These are intravascular agents that have a rheology similar to red blood cells and thus allow analysis of myocardial blood flow both at rest and after stress. The combined assessment of wall motion and myocardial perfusion provides significant diagnostic and prognostic information during stress echocardiography. Functional imaging tests, such as myocardial perfusion scintigraphy and stress cardiac magnetic resonance imaging, are also used for non-invasive assessment of coronary disease. The principal advantages of MCE are that it does not expose the patient to ionising radiation or radioactive pharmaceuticals, is not contraindicated in patients with an implanted metallic device or who suffer from claustrophobia and it can be performed at the bedside. The purpose of this article is to outline the physiological principles underpinning ischaemia testing with MCE before proceeding to review the evidence base for MCE in patients with known or suspected coronary artery disease.

Sign in / Sign up

Export Citation Format

Share Document