Prediction of 3-month treatment outcome of IgG4-DS based on BP artificial neural network.

Objective: The study aimed to establish an effective back-Propagation artificial neural network (BP-ANN) model for automatic prediction of 3-month treatment outcome of IgG4-DS.

Methods: A total of 26 IgG4-DS patients at Shanghai Ninth People's Hospital from January 2018 to December 2019 were involved in the study. They were all followed for >3 months. The primary outcome was reduction of serum IgG4 (sIgG4) after 3-month treatment. The association between risk factors and reduction of sIgG4 was analyzed by Spearman's rank correlation test. According to the R values, we built a BP-ANN model by MATLAB R2019b.

Results: The average reduction of sIgG4 was 5.55 ± 5.03. After Spearman's rank correlation test, ESR, sIgG4, and sIgG were independently associated with reduction of sIgG4 (p < .05) and were selected as input variables. Take into account these parameters, BP-ANN model was developed and the coefficient of determination (R ) model was 0.95512.

Conclusion: The BP-ANN model based on ESR, sIgG4, and sIgG could predict the 3-month reduction of sIgG4 for IgG4-DS patients. It showed potential clinical application value.