Application of Machine Learning Methods in Liver Cirrhosis Prediction

DOI

10.2991/978-94-6239-664-7_21How to use a DOI?

Keywords

Liver Cirrhosis; SMOTE; RF; XGBoost; Machine Learning

Abstract

Liver cirrhosis is typically described as the end stage of chronic hepatic disease, in which progressive and irreversible deposition of fibrotic tissue leads to the progressive hepatic dysfunction. Early diagnosis of cirrhosis at an early stage gives a significant benefit, as it allows timely therapeutic interventions and the introduction of a complex of health management measures. In order to assess the opportunities of machine learning methods in this scenario, we designed an automated predictive model and compared the performances of various algorithms, such as the Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Light Gradient Boosting Machine (LGBM). The empirical study was based on a dataset of 584 patient records that were annotated with eleven salient clinical variables. The findings showed that XGBoost had the best performance measures with an accuracy of 82.32%, an F1-score of 83.78%, a sensitivity of 91.46% and a precision of 77.32%. Both LGBM and GBM also demonstrated good predictive abilities. These results highlight the potential usefulness of machine learning tools to complement the accuracy of early diagnosis and clinical management of patients with liver cirrhosis.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rehana Parvin
AU  - Mst. Rashida Pervin
AU  - Mohammed Motaher Hossain
AU  - Raffat Arman Islam
PY  - 2026
DA  - 2026/06/08
TI  - Application of Machine Learning Methods in Liver Cirrhosis Prediction
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 285
EP  - 313
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_21
DO  - 10.2991/978-94-6239-664-7_21
ID  - Parvin2026
ER  -