Dataset Information

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 ([Formula: see text]) for the identification of patients with fetal loss outcomes.