Artificial Neural Network Models for Coronary Artery Disease

(E-pub Ahead of Print)

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, Majid Ghayour-Mobarhan*

Journal Name: Current Bioinformatics

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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

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1574893615666200214102837
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