Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models

doi:10.4254/wjh.v18.i3.117465

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World Journal of Hepatology

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1948-5182

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Hepatol. Mar 27, 2026; 18(3): 117465
Published online Mar 27, 2026. doi: 10.4254/wjh.v18.i3.117465

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Figure 1 Methodological workflow of the study. HCV: Hepatitis C virus; HBV: Hepatitis B virus; HIV: Human immunodeficiency virus; LASSO: Least absolute shrinkage and selection operator; RF: Random forest; SVM: Support vector machine; KNN: K-nearest neighbors; NB: Naive Bayes; APRI: Aspartate aminotransferase to platelet ratio index; FIB-4: Fibrosis-4 index; GPR: Gamma-glutamyl transferase to platelet ratio.

Citation: Bashir A, Arora R, Mehrotra D, Bala M, Parry AH, Iqball A, Bhat SA, Wani ZA. Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models. World J Hepatol 2026; 18(3): 117465
URL: https://www.wjgnet.com/1948-5182/full/v18/i3/117465.htm
DOI: https://dx.doi.org/10.4254/wjh.v18.i3.117465

Bashir A, Arora R, Mehrotra D, Bala M, Parry AH, Iqball A, Bhat SA, Wani ZA. Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models. World J Hepatol 2026; 18(3): 117465 [DOI: 10.4254/wjh.v18.i3.117465]

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