Use of artificial neural network to predict esophageal varices in patients with HBV related cirrhosis

Background: prediction of esophageal varices in cirrhotic patients by noninvasive methods is still unsatisfactory.objectives: to evaluate the accuracy of an artificial neural network (ann) in predicting varices in patients with hbv related cirrhosis.patients and methods: an ann was constructed with data taken from 197 patients with hbv related cirrhosis. the candidates for input nodes of the ann were assessed by univariate analysis and sensitivity analysis. five-fold cross validation was performed to avoid over-fitting.results: 14 variables were reduced by univariate and sensitivity analysis, and an ann was developed with three variables (platelet count, spleen width and portal vein diameter). with a cutoff value of 0.5. the ann model has a sensitivity of 96.5%, specificity of 60.4%, positive predictive value of 86.9%, negative predictive value of 86.5% and a diagnostic accuracy of 86.8% for the prediction of varices.conclusions: an ann may be useful for predicting presence of esophageal varices in patients with hbv related cirrhosis.