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Prediction of lupus nephritis in patients with systemic lupus erythematosus using artificial neural networks

School of Intelligent Systems, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran; Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; School of Intelligent Systems, Institute for Studies in Theoretical Physics and Mathematics, Niavaran, PO Box 19395-5746, Tehran, Iran.; rajimehr{at}ipm.ir

S Farsiu

Department of Electrical and Computer Engineering, Faculty of Technology, Tehran University, Tehran, Iran

L Montaser Kouhsari

School of Intelligent Systems, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran; Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

A Bidari

Section of Rheumatology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

C Lucas

School of Intelligent Systems, Institute for Studies in Theoretical Physics and Mathematics, Tehran, Iran; Department of Electrical and Computer Engineering, Faculty of Technology, Tehran University, Tehran, Iran

S Yousefian

Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran

F Bahrami

Department of Electrical and Computer Engineering, Faculty of Technology, Tehran University, Tehran, Iran

Arti" cial neural networks are intelligent systems that have been successfully used for prediction in different medical " elds. In this study, ef" ciency of neural networks for prediction of lupus nephritis in patients with systemic lupus erythematosus (SLE) was compared with a logistic regression model and clinicians’diagnosis. Overall accuracy, sensitivity and speci" city of the optimal neural network were 68.69, 73.77 and 62.96%, respectively. Overall accuracy of neural network was greater than the other two methods (P-value< 0.05). The neural network was more speci" c in predicting lupus nephritis (P-value < 0.01), but there was no signi" cant difference between sensitivities of the three methods. Sensitivities of all three methods were greater than their speci" cities. We concluded that neural networks are ef" cient in predicting lupus nephritis in SLE patients.

Key Words: arti" cial neural network • lupus nephritis • prediction • regression analysis

Lupus, Vol. 11, No. 8, 485-492 (2002)
DOI: 10.1191/0961203302lu226oa


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