MACHINE LEARNING SCORES ACCURATELY CLASSIFY INDIVIDUALS AT INDETERMINATE RISK OF INCIDENT CIRRHOSIS INTO LOW AND HIGH RISK GROUPS
<div><p><b>Background: </b>Risk stratification in non-alcoholic fatty liver disease (NAFLD) using non-invasive scores including Fibrosis-4 (FIB4) and NAFLD fibrosis score (NFS) is recommended by clinical guidelines. However, FIB4 and NFS values are indeterminate in 20-50% of patients with NAFLD. We aimed to develop machine learning models to improve upon FIB4 and NFS, especially in the indeterminate-risk range.</p>