Machine Learning-Based Prediction of Decompensation in Hepatitis B Virus-Related Cirrhosis.

ABSTRACT: Background/Objectives: Fatality of cirrhotic patients greatly increases when they progress to the decompensated state. Only a few studies to date have applied machine learning (ML) methods to predict decompensation in cirrhosis patients. In the present study, we attempted to apply self-developed ML models for validating their capability of predicting different complications in hepatitis B virus (HBV)-related cirrhosis patients. Methods: Data were extracted from electronic health records of 50,047 patients who were tested and diagnosed with HBV in a tertiary hospital. Four different algorithms (Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF)) were utilized, and a total of 32 ML models were trained and tested to predict variceal bl

SUBMITTER: Lin HC 

PROVIDER: S-EPMC12610556 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature