A novel model for predicting fatty liver disease by means of an artificial neural network

Background:The artificial neural network(ANN)emerged recently as a potent diagnostic tool,especially for complicated systemic diseases.This study aimed to establish a diagnostic model for the recognition of fatty liver disease(FLD)by virtue of the ANN.Methods:A total of 7,396 pairs of gender-and age-matched subjects who underwent health check-ups at the First Affiliated Hospital,College of Medicine,Zhejiang University(Hangzhou,China)were enrolled to establish the ANN model.Indices available in health check-up reports were utilized as potential input variables.The performance of our model was evaluated through a receiver-operating characteristic(ROC)curve analysis.Other outcome measures included diagnostic accuracy,sensitivity,specificity,Cohen’s k coefficient,Brier score,and Hosmer-Lemeshow test.The Fatty Liver Index(FLI)and the Hepatic Steatosis Index(HSI),retrained using our training-group data with its original designated input variables,were used as comparisons in the capability of FLD diagnosis.Results:Eight variables(age,gender,body mass index,alanine aminotransferase,aspartate aminotransferase,uric acid,total triglyceride,and fasting plasma glucose)were eventually adopted as input nodes of the ANN model.By applying a cut-off point of 0.51,the area under ROC curves of our ANN model in predicting FLD in the testing group was 0.908[95%confidence interval(CI),0.901-0.915]—significantly higher(P<0.05)than that of the FLI model(0.881,95%CI,0.872-0.891)and that of the HSI model(0.885;95%CI,0.877-0.893).Our ANN model exhibited higher diagnostic accuracy,better concordance with ultrasonography results,and superior capability of calibration than the FLI model and the HSI model.Conclusions:Our ANN system showed good capability in the diagnosis of FLD.It is anticipated that our ANN model will be of both clinical and epidemiological use in the future.