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.

• Implication for health policy/practice/research/medical education:
  Though the upper gastrointestinal endoscopy remains the gold standard for the diagnosis of gastroesophageal varices, non invasive diagnostic means are desired to reduce the frequency of endoscopic examinations and related costs. Reading this article is recommended to all internists, gastroenterologists and hepatologists.
• Please cite this paper as:
  Hong W, Ji Y, Wang D, Chen T, Zhu Q. Use of artificial neural network to predict esophageal varices in patients with HBV-related cirrhosis. Hepat Mon. 2011;11(7): 544-7.

© 2011 Kowsar M.P.Co. All rights reserved.