Learning from imbalanced fetal outcomes of systemic lupus erythematosus in artificial neural networks

Abstract

Objective: To explore an effective algorithm based on artificial neural network to pick correctly the minority of pregnant women with SLE suffering fetal loss outcomes from the majority with live birth and train a well behaved model as a clinical decision assistant. Methods: We integrated the thoughts of comparative and focused study into the artificial neural network and presented an effective algorithm aiming at imbalanced learning in small dataset. Results: We collected 469 non-trivial pregnant patients with SLE, where 420 had live-birth outcomes and the other 49 patients ended in fetal loss. A well trained imbalanced-learning model had a high sensitivity of 19/21 (90.8 %) for the identification of patients with fetal loss outcomes. Discussion: The misprediction of the two patients was explainable. Algorithm improvements in artificial neural network framework enhanced the identification in imbalanced learning problems and the external validation increased the reliability of algorithm. Conclusion: The well-trained model was fully qualified to assist healthcare providers to make timely and accurate decisions.