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

Abstract

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.