Determining the progression stages of liver fibrosis in patients with chronic hepatitis B

Introduction: Chronic hepatitis B (CHB) leads to liver fibrosis, its failure, and death in the long term. The stage of fibrosis in CHB patients can also be detected based on the biochemical markers. The aim of this study was to predict the state of liver fibrosis in CHB patients and determine the possibility of patients shifting from a given state to another one. Materials and Methods: This study is a cross-sectional study conducted in 2021. Age, blood platelet count, AST, and ALT enzymes were used as the input variable to create predictive models. Predictive models were Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), and Neural Network (NN). The probability of a patient shifting from a given stage of fibrosis to another was calculated using the transition matrix. The 10-fold cross-validation was used to ensure the generalization of predictive models. Results: The DT had the best precision, recall, and accuracy (100%) among developed algorithms to predict the stage of fibrosis in CHB patients. The NN was the second most efficient algorithm. Its accuracy and mean square error was 99.35±0.60 and 0.058±0.025, respectively. Besides, SVM had the lowest recall, precision, and accuracy values. Based on the transition matrix results, there is a very low probability that the patients with non-significant fibrosis state shifted to the cirrhosis state. Conclusion: Computational approaches like machine learning algorithms are the non-invasive way to predict the fibrosis state in CHB patients efficiently.