Volume 73, Issue 4 (July 2015)                   Tehran Univ Med J 2015, 73(4): 251-259 | Back to browse issues page

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Akbarian M, Paydar K, ostam Niakan Kalhori S R, Sheikhtaheri A. Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran. Tehran Univ Med J. 2015; 73 (4) :251-259
URL: http://tumj.tums.ac.ir/article-1-6730-en.html
1- Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran.
2- Department of Health Information Management, School of Allied medical sciences, Tehran University of Medical Sciences, Tehran, Iran.
3- Department of Public Health, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
4- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran. , sheikhtaheri.a@iums.ac.ir
Abstract:   (3619 Views)
Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables.
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Type of Study: Original Article |

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