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Abstract Details
Non-alcoholic Fatty Liver and Liver Fibrosis Predictive Analytics: Risk Prediction and Machine Learning Techniques for Improved Preventive Medicine
J Med Syst. 2021 Jan 11;45(2):22. doi: 10.1007/s10916-020-01693-5.
Orit Goldman1, Ofir Ben-Assuli2, Ori Rogowski3, David Zeltser3, Itzhak Shapira3, Shlomo Berliner3, Shira Zelber-Sagi45, Shani Shenhar-Tsarfaty3
Author information
1Faculty of Business Administration, Ono Academic College, 104 Zahal Street, 55000, Kiryat Ono, Israel. oritgol@bezeqint.net.
2Faculty of Business Administration, Ono Academic College, 104 Zahal Street, 55000, Kiryat Ono, Israel.
3Departments of Internal Medicine "C", "D" and "E", Tel-Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel-Aviv University, Weizmann 6 St, Tel Aviv, Israel.
4School of Public Health, University of Haifa, 3498838, Haifa, Israel.
5Department of Gastroenterology, Tel Aviv Medical Center, 6423906, Tel Aviv, Israel.
Abstract
Non-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' calculator (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 subjected 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 disease burden. Interventions to prevent chronic disease can substantially reduce medical complications and the costs of the disease. The findings highlight the value of predictive analytic tools in health care environments. NAFLD constitutes a growing burden on the health system; thus, identification of the factors related to its incidence can make a strong contribution to preventive medicine.