Machine learning approaches for optimal prediction of liver fibrosis cruelty

Abstract


 
 
 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. 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 classier. The resulting classifiers prospectively tested in 50% testing patients, and the sensitivity, septicity, 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.