Machine Learning Model for Diagnosing the Stage of Liver Fibrosis in Patients With Chronic Viral Hepatitis C

Abstract

Aim. The purpose of the work was the development of a machine learning model for diagnosing the stage of liver fibrosis in patients with chronic viral hepatitis C according to the data of routine clinical examination. Materials and methods. A total of 1240 patients with chronic viral hepatitis C was examined. A set of data obtained from 689 patients balancing by the stage of liver fibrosis was used for developing and testing machine learning models. 9 routine clinical parameters were selected as the most important predictors for determining the likelihood of liver fibrosis the 3–4 stages presence: age, height, weight and body mass index of the patient, the number of platelets in the clinical blood test, levels of alanine transaminase, aspartate transaminase, gamma-glutamyltransferase, and total bilirubin in a biochemical blood test. Results. The accuracy of the developed method for determining the 3–4 stages of liver fibrosis in patients with chronic viral hepatitis C in comparison with the «gold standard» of diagnosis (liver biopsy) was 80.56% (95% CI: 69.53–88.94%), sensitivity — 66.67%, specificity — 94.44%. Conclusion. The developed method is an alternative to more expensive and geographically inaccessible studies. The method does not require the purchase of additional equipment or software, as well as additional laboratory tests, when used in real clinical practice. The introduction of the method into clinical practice can help to solve the problem of low material and territorial availability of diagnostic tests and allow determining the stage of liver fibrosis in patients with chronic viral hepatitis C.

hyOPTGB: An Efficient OPTUNA Hyperparameter Optimization Framework for Hepatitis C Virus (HCV) Disease Prediction in Egypt

“…Various machine learning classification models have been applied in previous studies for HCV prediction, and this section presents a discussion of their results. In a study by Tsvetkov et al [22], a machine learning model was proposed to diagnose the stage of liver fibrosis in patients. The authors analyzed 1240 patient records with chronic viral Hepatitis C and developed machine learning models using data from 689 patients classified by the stage of liver fibrosis.…”

Diagnosing the Stage of Hepatitis C Using Machine Learning

“…Essential predictors were chosen from nine usual prognostic factors. They achieved the highest accuracy of 80.56% [ 9 ].…”

“…All the previously presented approaches have a few of the weaknesses mentioned below: Low accuracy: Most of the approaches in literature [ 9 , 11 , 14 ] have achieved low accuracy (not even above 90% in some cases). It gets difficult for the doctors and patients to rely on these results only.…”

“…It gets difficult for the doctors and patients to rely on these results only. Experiments with fewer parameters: Some of the previously presented methods [ 9 , 11 , 14 ] have used a fewer number of parameters. They skip a few of the important factors in the prediction of fibrosis in a liver.…”

Integrated neural network and evolutionary algorithm approach for liver fibrosis staging: Can artificial intelligence reduce patient costs?

“…AI can be used for prediction by classification methods using data mining and machine learning (ML) algorithms. Several AI projects have been conducted in order to predict the staging of hepatitis C, including some ML methods 10–12 . However, most of these have used a small number of parameters or could not achieve high accuracy.…”