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
Published online Mar 27, 2026. doi: 10.4254/wjh.v18.i3.117465
Non-invasive prediction of significant hepatic fibrosis in individuals with chronic hepatitis C infection using fibrosis risk score and machine learning models
Azra Bashir, Renuka Arora, Department of Computer Science and Engineering, Amity University, Noida 201303, Uttar Pradesh, India
Deepti Mehrotra, Jaypee Institute of Information Technology, Noida 201303, Uttar Pradesh, India
Manju Bala, Department of Computer Science, Indraprastha College for Women, Delhi University, New Delhi 110054, Delhi, India
Arshed H Parry, Shabir A Bhat, Department of Radiodiagnosis and Imaging, Government Medical College, Srinagar 190010, Jammu and Kashmir, India
Asif Iqball, Zeeshan A Wani, Department of Gastroenterology, Government Medical College, Srinagar 190010, Jammu and Kashmir, India
Author contributions: Bashir A, Arora R, Mehrotra D, and Bala M conceptualized the study and performed the machine learning; Bashir A, Parry AH, Iqball A, Bhat SA, and Wani ZA collected and interpreted clinical data; Bashir A, Arora R, Mehrotra D, Bala M, and Parry AH wrote and edited the manuscript; and all authors contributed to manuscript revision and provided approval for publishing the final version of the manuscript.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Government Medical College, Srinagar, Jammu and Kashmir, approval No. IRBGMC-SGR/Radio/1358.
Informed consent statement: Owing to the retrospective design of study, informed consent from the patients was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
Corresponding author: Arshed H Parry, MD, Assistant Professor, Department of Radiodiagnosis and Imaging, Government Medical College, 10, Karanagar, Srinagar 190010, Jammu and Kashmir, India. arshedparry@gmail.com
Received: December 8, 2025
Revised: December 28, 2025
Accepted: February 2, 2026
Published online: March 27, 2026
Processing time: 108 Days and 11.6 Hours
Revised: December 28, 2025
Accepted: February 2, 2026
Published online: March 27, 2026
Processing time: 108 Days and 11.6 Hours
Core Tip
Core Tip: This study developed and validated a simple non-invasive fibrosis risk score for hepatitis C virus using platelet count, lipid markers, and liver function parameters. The score outperformed commonly used clinical tools for detecting significant hepatic fibrosis. Additionally, machine-learning models were evaluated, which outperformed the commonly used clinical risk scoring systems with random forest and AdaBoost demonstrating the highest diagnostic performance for detecting significant hepatic fibrosis in hepatitis C virus infection.