EXPLAINABLE AI AND MACHINE LEARNING FOR EARLY DIAGNOSIS OF LIVER CIRRHOSIS: A COMPREHENSIVE REVIEW
AUTHORS:
Mentor
Affiliation
Department of Computer Science and Engineering Gautam Buddha University Greater Noida, Uttar Pradesh
DOI: 
SHARE THIS ARTICLE
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
DOWNLOAD ARTICLE
REVIEW REPORT
CITE THIS ARTICLE
Abstract
Liver cirrhosis is a chronic, progressive, and po- tentially fatal disease resulting from chronic liver damage. The detection in early phase asymptomatic difficult, which making timely diagnosis difficult and caused the sever complication or mortality. Due to Block-box nature of the machine learning model poses a challenge for clinical practitioners to lies these ML models. Liver disease prediction is major clinical issue, in India approximately 1 million cirrhosis cases reported every year with high mortality and worldwide 58.4 million case reported in 2021 and 1.4 million mortality count. The Global Burden of Disease (GBD) database shows that liver cirrhosis increase 58.21 % from 1990 to 2021 . The use of Explainable AI (XAI) and Machine learning (ML) techniques help in detecting liver cirrhosis disease at early phase, thereby minimizing the severity of the disease. This literature study review recent advances (2015–2025) in XAI and ML models for liver cirrhosis detection, highlighting the role in early diagnosis, risk assessment, and automated medical image analysis. This review also addressing challenges related to model transparency, data heterogeneity, and clinical translation.
Keywords
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 03
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Mar 04 2026
Article Metrics
Article Views
67
PDF Downloads
3
HOW TO CITE
Copy
Kumar, K. K. C. ,. A. (2026). Explainable AI and Machine Learning for Early Diagnosis of Liver Cirrhosis: A Comprehensive Review. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.008
Kumar, Krishan. "Explainable AI and Machine Learning for Early Diagnosis of Liver Cirrhosis: A Comprehensive Review." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.008.
Kumar, Krishan. "Explainable AI and Machine Learning for Early Diagnosis of Liver Cirrhosis: A Comprehensive Review." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.008.
References
1.A. Author and B. Researcher, “The comprehensive role of the liver in human metabolism,” Journal of Hepatology, vol. 72, no. 4, pp. 623–635, 2020.
2.C. Scientist and D. Investigator, “Mechanisms of alcohol and viral hepatitis in liver injury,” Liver International, vol. 39, no. 2, pp. 245–258, 2019.
3.E. Writer and F. Scholar, “Pathophysiology of liver cirrhosis: From scarring to failure,” Nature Reviews Gastroenterology & Hepatology, vol. 18, no. 6, pp. 401–415, 2021.
4.G. Clinician and H. Physician, “The silent epidemic: Challenges in diagnosing asymptomatic liver disease,” Clinical Gastroenterology and Hepatology, vol. 16, no. 9, pp. 1370–1372, 2018.
5.I. Researcher and J. Analyst, “Mortality and complications associated with late diagnosis of cirrhosis,” Hepatology, vol. 75, no. 3, pp. 588–600, 2022.
6.K. Doctor and L. Specialist, “The burden of liver cirrhosis in india: A 2023 update,” Indian Journal of Gastroenterology, vol. 42, no. 1, pp. 15–25, 2023.
7.G. . C. Collaborators, “Global, regional, and national burden of liver cirrhosis, 1990-2021: a systematic analysis for the global burden of disease study 2021,” The Lancet Gastroenterology & Hepatology, vol. 7, no. 9, pp. 796–815, 2022.
8.M. Engineer and N. Programmer, “An introduction to machine learning in healthcare,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1359–1370, 2019.
9.O. Analyst and P. Scientist, “Integrating multimodal data for liver disease assessment,” Computers in Biology and Medicine, vol. 122, p. 103839, 2020.
10.Q. Reviewer and R. Expert, “Machine learning for early detection of liver diseases: A systematic review,” Artificial Intelligence in Medicine, vol. 148, p. 102762, 2024.
2.C. Scientist and D. Investigator, “Mechanisms of alcohol and viral hepatitis in liver injury,” Liver International, vol. 39, no. 2, pp. 245–258, 2019.
3.E. Writer and F. Scholar, “Pathophysiology of liver cirrhosis: From scarring to failure,” Nature Reviews Gastroenterology & Hepatology, vol. 18, no. 6, pp. 401–415, 2021.
4.G. Clinician and H. Physician, “The silent epidemic: Challenges in diagnosing asymptomatic liver disease,” Clinical Gastroenterology and Hepatology, vol. 16, no. 9, pp. 1370–1372, 2018.
5.I. Researcher and J. Analyst, “Mortality and complications associated with late diagnosis of cirrhosis,” Hepatology, vol. 75, no. 3, pp. 588–600, 2022.
6.K. Doctor and L. Specialist, “The burden of liver cirrhosis in india: A 2023 update,” Indian Journal of Gastroenterology, vol. 42, no. 1, pp. 15–25, 2023.
7.G. . C. Collaborators, “Global, regional, and national burden of liver cirrhosis, 1990-2021: a systematic analysis for the global burden of disease study 2021,” The Lancet Gastroenterology & Hepatology, vol. 7, no. 9, pp. 796–815, 2022.
8.M. Engineer and N. Programmer, “An introduction to machine learning in healthcare,” IEEE Journal of Biomedical and Health Informatics, vol. 23, no. 4, pp. 1359–1370, 2019.
9.O. Analyst and P. Scientist, “Integrating multimodal data for liver disease assessment,” Computers in Biology and Medicine, vol. 122, p. 103839, 2020.
10.Q. Reviewer and R. Expert, “Machine learning for early detection of liver diseases: A systematic review,” Artificial Intelligence in Medicine, vol. 148, p. 102762, 2024.
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
Artificial Intelligence in Supply Chain Risk Management: A Systematic Literature Review
(2026)
DOI: 10.55041/ijsmt.v2i3.033
राष्ट्र-निर्माण में गाँव की भूमिका : गांधी और नेहरू की दृष्टि का तुलनात्मक अध्ययन
(2026)
DOI: 10.55041/ijsmt.v2i3.054
Antidiabetic and Antihyperlipidemic Activity of Herbal Extract in Streptozotocin-Induced Model
(2026)
DOI: 10.55041/ijsmt.v2i1.003
Previous Article
