OVERVIEW OF LIVER FIBROSIS DETECTION METHOD USING MACHINE LEARNING APPROACHES
DOI:
https://doi.org/10.46754/jmsi.2025.12.006Keywords:
Liver fibrosis, hepatitis, machine learningAbstract
Liver fibrosis is a chronic illness that results from chronic liver diseases such as hepatitis, cirrhosis, haemochromatosis, and non-alcoholic fatty liver disease (NAFLD). For timely detection and improved patient outcomes, liver fibrosis must be accurately staged for effective patient management and treatment. Traditional diagnostic methods such as liver biopsies, have risks and are invasive, among other downsides. However, recent advances in Machine Learning (ML) have offered substitutes to detect liver fibrosis. ML approaches are emerging as effective tools for the non-invasive detection of liver fibrosis; they have the potential to increase detection accuracy and reduce the demand for invasive liver biopsies. This overview provides a summary of methods for detecting liver fibrosis, with a particular emphasis on ML and both traditional and contemporary assessment methods. In recent years, many machine learning algorithms have been used to predict liver fibrosis detection for the Genetic Algorithm (GA), Artificial Neural Network (ANN), Naïve Bayes, and Multi-linear Regression, as well as Random Forest, Genetic techniques, Decision Tree (DT), Support Vector Machine (SVM), and Particle Swarm Optimisation (PSO). Besides, the results and performance of ML approaches are reviewed with a comparison of existing research studies, which were used for the detection of liver fibrosis. This review provides a roadmap that will assist researchers in making the most of the extensive capabilities of machine learning algorithms to build secure predictive models.
References
Singh, H. R., & Rabi, S. (2019). Study of morphological variations of liver in human. Translational Research in Anatomy, 14, 1-5.
Bojanic, K., Bogojevic, M. S., Vukadin, S., Sikora, R., Ivanac, G., Lucic, N. R., Smolic, M., Tabll, A., Wu, G. Y., & Smolic, R. (2023). Noninvasive fibrosis assessment in chronic hepatitis C infection. Journal of Clinical and Translational Hepatology, 11(5), 1228-1238. DOI: 10.14218/JCTH.2022.00365.
Sterling, R. K., Lissen, E., Clumeck, N., Sola, R., Correa, M. C., Montaner, J., Sulkowski, M. S., Torriani, F. J., Dieterich, D. T., Thomas, D. L., Messinger, D., & Nelson, M. (2006). Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology, 43(6), 1317-1325.
Wai, C. T., Greenson, J. K., Fontana, R. J., Kalbfleisch, J. D., Marrero, J. A., Conjeevaram, H. S., & Lok, A. S. (2025). A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology, 38, 518-526.
Dreytser, E., Blyuss, O., Mudrova, A., Turankova, T., Gurusamy, K. S., & Pavlov, C. S. (2025). Aspartate aminotransferase‐to‐platelet ratio index (APRI) for staging of fibrosis in adults with chronic hepatitis C. Cochrane Database of Systematic Reviews, 2025(8).
Nguyen, T., Khosravi, A., Creighton, D., & Nahavandi, S. (2025). Medical data classification using interval type-2 fuzzy logic system and wavelets. Applied Soft Computing, 30, 812-822.
Garcia-Saenz-de-Sicilia, M., Al-Obaid, L., Hughes, D. L., & Duarte-Rojo, A. (2022, August). Mastering core recommendations during HEPAtology ROUNDS in patients with advanced chronic liver disease. In Seminars in Liver Disease. Thieme Medical Publishers Inc.
Hashem, S., Esmat, G., Elakel, W., Habashy, S., Abdel Raouf, S., Darweesh, S., Soliman, M., Elhefnawi, M., El-Adawy, M., & ElHefnawi, M. (2016). Accurate prediction of advanced liver fibrosis using the decision tree learning algorithm in chronic hepatitis C Egyptian patients. Gastroenterology Research and Practice, 2016, 1–7. https://doi.org/10.1155/2016/2636390.
Hudson, D., Afzaal, T., & Bualbanat, H. (2024). Modernising metabolic dysfunction-associated steatotic liver disease diagnostics: the progressive shift from liver biopsy to noninvasive techniques. Therapeutic Advances in Gastroenterology, 17 DOI:10.1177/17562848241276334.
Rouis, S., Mrabet, S., Ferjaoui, M., Ben Lasfar, N., Sahli, J., Boujamline, S., Ayari, R., Abid, M., Ben Selma, M., Ben Ticha, M., Bellazreg, F., Ben Jezia, E., Letaeif, A., & Hachfi, W. (2025). EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis. Journal of Hepatology, 63(1), 237-264.
Castera, L., & Pinzani, M. (2010). Biopsy and non-invasive methods for the diagnosis of liver fibrosis: Does it take two to tango? Gut, 59(7), 861-866.
Younossi, Z. M., Golabi, P., Paik, J. M., Henry, A., Van Dongen, C., & Henry, L. (2023). The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): A systematic review. Hepatology, 77(4), 1335-1347.
Navin, P. J., & Venkatesh, S. K. (2019). Hepatocellular carcinoma: State of the art imaging and recent advances. Journal of Clinical and Translational Hepatology, 7(1), 72.
Razia, S., Prathyusha, P. S., Krishna, N. V., & Sumana, N. S. (2017). A review on disease diagnosis using machine learning techniques. International Journal of Pure and Applied Mathematics, 117(16), 79-85.
Shaheamlung, G., & Kaur, H. (2021). The diagnosis of chronic liver disease using machine learning techniques. Information Technology in Industry, 9(2), 554-564.
Sharshar, E. T., Amin, H., & Badr, N. (2023). Survey of liver fibrosis prediction using machine learning techniques. International Journal of Intelligent Computing and Information Sciences, 23(2), 1-12.
Kadir, T., & Gleeson, F. (2018). Lung cancer prediction using machine learning and advanced imaging techniques. Translational Lung Cancer Research, 7(3), 304.
Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
Tapper, E. B., & Lok, A. S. -F. (2017). Use of liver imaging and biopsy in clinical practice. New England Journal of Medicine, 377(8), 756-768.
Berrar, D. (2018). Bayes’ Theorem and Naive Bayes Classifier. Encyclopedia of bioinformatics and computational biology: ABC of bioinformatics (pp. 403). Amsterdam, The Netherlands, Elsevier Science Publisher.
Abdar, M., Yen, N. Y., & Hung, J. C. -S. (2018). Improving the diagnosis of liver disease using multilayer perceptron neural network and boosted decision trees. Journal of Medical and Biological Engineering, 38, 953-965.
Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215.
Singh, A., & Pandey, B. (2018). An efficient diagnosis system for detection of liver disease using a novel integrated method based on principal component analysis and k-nearest neighbor (pca-knn), in Intelligent Systems: Concepts, methodologies, tools, and applications. IGI Global, 1015-1030.
AbuSharekh, E. K., & Abu-Naser, S. S. (2018). Diagnosis of hepatitis virus using artificial neural network.
Agarwal, G. G., Singh, A. K., Venkatesh, V., & Wal, N. (2019). Determination of risk factors for hepatitis C by the method of random forest. Annal of Infectious Disease and Epidemiology, 4(1).
Ghazal, T. M. (2021). Hep-pred: Hepatitis C staging prediction using fine gaussian svm. Computers, Materials & Continua, 69(1), 191-203.
Cao, Y., Hu, Z. D., Liu, X. F., Deng, A. M., & Hu, C. J. (2013). An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters. Disease Markers, 35, 653-660.
Akella, A., & Akella, S. (2020). Applying machine learning to evaluate for fibrosis in chronic hepatitis C. MedRxiv. DOI:10.1101/2020.11.02.20224840
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