Using Artificial Neural Network to Predict Cirrhosis in Patients with Chronic Hepatitis B Infection with Seven Routine Laboratory Findings

Chronic liver diseases could lead to cirrhosis and related complications. Histological diagnosis byliver biopsy has long been the gold standard for assessing the degree of fibrosis and diagnosis of cirrhosis, but it is aninvasive procedure with inherent risk and sampling variability. The aim of this study was to assess the ability of the artificialneural network (ANN) to predict the presence or absence of cirrhosis in patients with chronic hepatitis B by usingroutine laboratory findings.Methods: 114 chronic hepatitis B patients who were admitted between 1996 and 2006 at Loghman Hakim hospital andthe Tehran Hepatitis Center were evaluated. The disease was confirmed by hepatitis B virus (HBV) DNA, liver biopsies,and biochemistry values, which were obtained from all of patients. Back propagation ANN analyses were carried out bytraining the networks with the data. The patients were divided into two groups. The first group (92 patients) includedtwo thirds of the cirrhotic patients (12 patients) and two thirds of the non-cirrhotic patients (80 patients) in a randomizedrout.Results: Ascitis, edema, pruritus, splenomegaly, and hepatomegaly were present in 26.3%, 31.6%, 21.1%, 63.2%, and0% of cirrhotic patients, respectively, and 0%, 0%, 3.2%, 2.4%, and 0.8% of non-cirrhotic patients, respectively. Thesensitivity, specificity, and positive and negative predictive ANN values in comparison with liver biopsy in the diagnosisof cirrhotic patients due to chronic HBV were 71.43%, 84.45%, 71.43%, and 95%, respectively.Conclusions: In chronic hepatitis B patients, if the ANN value of certain laboratory manifestations is negative, there willbe a 95% chance of having a negative liver biopsy.