Noninvasive assessment of severity of liver fibrosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. We aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classifier based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Logistic Regression Classifier (LRC), and Support Vector Classifier (SVC) for liver fibrosis severity assessment. Training and testing samples account for 50% of the data set, respectively. The best indicator combinations were selected in random combinations of 24 indicators including 67 108 760 group indicators by four different machine learning classifiers. The resulting classifiers prospectively tested in 50% testing patients, and the sensitivity, specificity, overall accuracy, and receiver operating characteristics (ROC) were used to compare four classifiers to existed 19 models. Results show that the RFC-based classifier system, with 9 indicators, is feasible to assess severity for liver fibrosis with diagnostic accuracy (greater than 0.83) superior to existing 19 models. Additional studies based on a large data set with full serum markers and imaging information are necessary to enhance diagnostic accuracy and to expand clinical application.

Information on the stage of liver fibrosis is essential for decisions on antiviral treatment for chronic hepatitis B virus (HBV). This paper aims to establish a non-invasive assessment model with serum markers using cloud computing and the Internet of Things for the evaluation of liver disease severity and its prognosis. Based on the Internet of Things, the multiple and key information system of liver fibrosis or cirrhosis are constructed using the serum markers data. In the cloud platform, the probability density functions of indexes are used to select the optimized indicators. The logistic regression is used to establish the non-invasive assessment model. The patients were selected with CHB and underwent liver biopsy in the Second Xiangya Hospital, Central South University. There are two inclusion criteria: first, the patient received a liver biopsy according to “Proclaim Prevention and Cure Guide For Chronic Hepatitis B”of Chinese Medical Association in 2015; second, the patient has a history of hepatitis B or HBV surface antigen (HBsAg) positive more than six months, and HBsAg and (or) HBV DNA is still positive. Results of clinical data applications show that the accuracy of the non-invasive assessment model reaches greater than 70% for the recognition of significant liver fibrosis. In addition, the discriminant accuracy can be improved by increasing the number of indicators. The established non-invasive assessment model can be used for auxiliary clinical diagnosis after the further validation.