Comparing the Efficiency of Artificial Neural Network and Gene Expression Programming in Predicting Coronary Artery DiseaseMoghaddasi H1*, Mahmoudi I2 and Sajadi S3
- *Corresponding Author:
- Moghaddasi H
Department of Health Information Technology and Management
School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences
Tel: 0098 2122747373
E-mail: [email protected]
Received Date: February 15, 2017; Accepted Date: March 06, 2017; Published Date: March 10, 2017
Citation: Moghaddasi H, Mahmoudi I, Sajadi S (2017) Comparing the Efficiency of Artificial Neural Network and Gene Expression Programming in Predicting Coronary Artery Disease. J Health Med Informat 8:250. doi: 10.4172/2157-7420.1000250
Copyright: © 2017 Moghaddasi H, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: Angiography, as the gold standard for the diagnosis of coronary artery disease, has made an attempt to predict coronary artery disease by comparing the efficiency of gene expression programming, as a new data mining technique, and artificial neural network, as a conventional technique. Besides, the study went further to present the results of feature selection based on stepwise backward elimination, classification and regression tree. Methods: The subjects were assessed for nine coronary artery disease risk factors to develop a prediction model for the disease. They included 13,288 patients who were chosen to undergo angiography for the diagnosis of coronary artery disease; from this sample, 4059 subjects were free from the disease while 9169 were suffering from it. Modeling was carried out based on gene expression programming and artificial neural network techniques. The Delong’s test was then used to choose the final model based on the area under the Receiver Operating Characteristic (ROC) curve. Results: The model, developed based on artificial neural network, had AUC of 0.719, accuracy of 73.39%, sensitivity of 93.44% and specificity of 28.34%. On the other hand, the model, formulated based on gene expression programming, had AUC of 0.720, accuracy of 73.94%, sensitivity of 93.29% and specificity of 31.43%. Delong’s test showed no significant difference between the two models (p value=0/789). Then, feature selection method was used to choose a model with four risk factors and an accuracy rate of 73.26%. Conclusion: Comparison of the results showed no significant difference between the two modeling techniques. The gene expression programming model was very easy to present and interpret; it could also be easily converted to other programming languages; so, with these features in mind, the researchers preferred to choose this technique.