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Artificial neural network models for coronary artery disease

Author(s):

Elham Shamsara, Sara Saffar Soflaei, Mohammad Tajfard, Ivan Yamshchikov, Habibollah Esmaili, Maryam Saberi-Karimian, Hamideh Ghazizadeh, Seyed Reza Mirhafez, Zahra Farjami, Gordon A. Ferns and Majid Ghayour-Mobarhan*  

Abstract:


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.

Keywords:

Coronary artery disease machine learning pattern recognition-ANN LVQ-ANN competitive ANN binarization technique

Affiliation:

Social Determinants of Health Research Center, Mashhad University of Medical Sciences, 91778-99191 Mash-had, Department of Modern Sciences & Technologies, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Department of Health Education and Health Promotion, School of Health, Mashhad University of Medical Sciences, Mashhad, Max-Planck-Institut für Mathematik in den Naturwissenschaften, Inselstr. 22, 04103 Leipzig, Department of Biostatistics and Epidemiology, School of Health, Mashhad University of Medical Sciences, Mashhad, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Department of Basic Medical Sciences, Neyshabur University of Medical Sciences, Neyshabur, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, Metabolic Syndrome Research Center, School of Medicine, Mashhad University of Medical Sciences, Mashhad



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