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.

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

scholarly journals A Machine Learning Model Utilizing a Novel SNP Shows Enhanced Prediction of Coronary Artery Disease Severity

Genes ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1446
Author(s):  
Tanyaporn Pattarabanjird ◽  
Corban Cress ◽  
Anh Nguyen ◽  
Angela Taylor ◽  
Stefan Bekiranov ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Severity Score ◽  
Clinical Decision Making ◽  
Confusion Matrix ◽  
Operating Characteristics ◽  
Increased Risk ◽  
Artery Disease

Background: Machine learning (ML) has emerged as a powerful approach for predicting outcomes based on patterns and inferences. Improving prediction of severe coronary artery disease (CAD) has the potential for personalizing prevention and treatment strategies and for identifying individuals that may benefit from cardiac catheterization. We developed a novel ML approach combining traditional cardiac risk factors (CRF) with a single nucleotide polymorphism (SNP) in a gene associated with human CAD (ID3 rs11574) to enhance prediction of CAD severity; Methods: ML models incorporating CRF along with ID3 genotype at rs11574 were evaluated. The most predictive model, a deep neural network, was used to classify patients into high (>32) and low level (≤32) Gensini severity score. This model was trained on 325 and validated on 82 patients. Prediction performance of the model was summarized by a confusion matrix and area under the receiver operating characteristics curve (ROC-AUC); and Results: Our neural network predicted severity score with 81% and 87% accuracy for the low and the high groups respectively with an ROC-AUC of 0.84 for 82 patients in the test group. The addition of ID3 rs11574 to CRF significantly enhanced prediction accuracy from 65% to 81% in the low group, and 72% to 84% in the high group. Age, high-density lipoprotein (HDL), and systolic blood pressure were the top 3 contributors in predicting severity score; Conclusions: Our neural network including ID3 rs11574 improved prediction of CAD severity over use of Framingham score, which may potentially be helpful for clinical decision making in patients at increased risk of complications from coronary angiography.

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