Volume 4 • Issue 2 • PP: 19–23 • 2025
Machine Learning-Based Liver Cirrhosis Prediction: A Review of Feature Selection, Optimization Algorithms, Clinical Applications, and Future Challenges
Open Access & Copyright
© 2025 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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Machine learning-based prediction has become an important research direction in the analysis of liver cirrhosis, where clinical, laboratory, and demographic data can be used to support early risk assessment, disease-stage identification, and outcome prediction. Liver cirrhosis represents a progressive and life-threatening condition associated with chronic hepatic injury, fibrosis, portal hypertension, liver-function deterioration, and several severe complications that may reduce patient survival and quality of life. Conventional diagnostic and prognostic approaches remain clinically valuable; however, they may be limited when dealing with complex, nonlinear, heterogeneous, and high dimensional patient data. This review examines the role of machine learning in liver cirrhosis prediction by analyzing how supervised learning, ensemble models, deep learning, feature-selection strategies, and optimization-based approaches contribute to improving classification accuracy, reducing irrelevant clinical variables, and enhancing model reliability. The review highlights major predictive tasks, including cirrhosis diagnosis, disease severity classification, survival prediction, complication-risk estimation, and treatment-outcome assessment. It also discusses how machine learning can assist clinical decision-making by identifying hidden relationships among biochemical markers, patient characteristics, and disease progression patterns that may not be easily captured through traditional statistical methods. Despite these advantages, the implementation of machine learning models for liver cirrhosis prediction remains associated with several challenges, including limited dataset size, class imbalance, missing clinical values, insufficient external validation, model interpretability concerns, data heterogeneity, and the need for clinically transparent decision-support systems. Overall, this review shows that machine learning-based prediction can provide a valuable computational framework for improving liver cirrhosis assessment when it is developed using reliable datasets, robust validation procedures, explainable modeling strategies, and clinically meaningful performance evaluation.
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