Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine

AbstractNon-alcoholic fatty liver disease (NAFLD) is the most common liver disease worldwide, with a prevalence of 20% –30% in the general population. NAFLD is associated with increased risk of cardiovascular disease and may progress to cirrhosis with time. The purpose of this study was to predict the risks associated with NAFLD and advanced fibrosis on the Fatty Liver Index (FLI) and the ‘NAFLD fibrosis 4’ ca lculator (FIB-4), to enable physicians to make more optimal preventive medical decisions. A prospective cohort of apparently healthy volunteers from the Tel Aviv Medical Center Inflammation Survey (TAMCIS), admitted for their routine annual health check-up. Data from the TAMCIS database were subject ed to machine learning classification models to predict individual risk after extensive data preparation that included the computation of independent variables over several time points. After incorporating the time covariates and other key variables, this technique outperformed the predictive power of current popular methods (an improvement in AUC above 0.82). New powerful factors were identified during the predictive process. The findings can be used for risk stratification and in planning future preventive strategies based on lifestyle modifications and medical treatment to reduce the diseas e burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive ana...
Source: Journal of Medical Systems - Category: Information Technology Source Type: research