Weighted Soft-Voting Ensembles for Liver Disease Prediction: A Large-Scale Comparative Study with Transparent Evaluation

Mohammad Ibraigheeth; Suhail Odeh; Mahmoud Obaid

doi:10.48084/etasr.16808

Authors

Mohammad Ibraigheeth Department of Software Engineering, Bethlehem University, Bethlehem, Palestine
Suhail Odeh Department of Software Engineering, Bethlehem University, Bethlehem, Palestine
Mahmoud Obaid Computer System Engineering Department, Arab American University, Jenin, Palestine

Volume: 16 | Issue: 2 | Pages: 32855-32860 | April 2026 | https://doi.org/10.48084/etasr.16808

Received: 8 December 2025 | Revised: 25 December 2025 and 6 January 2026 | Accepted: 9 January 2026 | Online: 4 April 2026

Corresponding author: Mohammad Ibraigheeth

Abstract

Early detection of liver disease can significantly improve patient outcomes and reduce healthcare costs. This study presents a comparative evaluation of four traditional machine learning classifiers—Logistic Regression, Support Vector Machines, Gaussian Naïve Bayes, and a Multi-Layer Perceptron—alongside an enhanced weighted soft-voting ensemble model. Using a large, publicly available clinical dataset (~30,000 records), a fully nested, leakage-free cross-validation framework is employed to ensure robust and reliable evaluation. The proposed ensemble assigns adaptive weights based on per-fold model performance and demonstrates superior discrimination and calibration compared to individual classifiers. The results highlight the contribution of transparent ensemble modeling in achieving accurate and clinically interpretable liver disease risk detection.

Keywords:

liver disease, weighted ensemble model, machine learning, traditional classifiers

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References

M. D. Leise, W. R. Kim, W. K. Kremers, J. J. Larson, J. T. Benson, and T. M. Therneau, "A Revised Model for End-Stage Liver Disease Optimizes Prediction of Mortality Among Patients Awaiting Liver Transplantation," Gastroenterology, vol. 140, no. 7, pp. 1952–1960, June 2011. DOI: https://doi.org/10.1053/j.gastro.2011.02.017

B. D. Ershoff et al., "Improving the Prediction of Mortality in the High Model for End-Stage Liver Disease Score Liver Transplant Recipient: A Role for the Left Atrial Volume Index," Transplantation Proceedings, vol. 50, no. 5, pp. 1407–1412, June 2018. DOI: https://doi.org/10.1016/j.transproceed.2018.03.017

A. Singh et al., "The development of the diabetes liver fibrosis score: A new prediction model to detect advanced fibrosis in diabetics with nonalcoholic fatty liver disease," Journal of Hepatology, vol. 68, pp. S98–S99, Apr. 2018. DOI: https://doi.org/10.1016/S0168-8278(18)30418-5

L. Saba et al., "Automated stratification of liver disease in ultrasound: An online accurate feature classification paradigm," Computer Methods and Programs in Biomedicine, vol. 130, pp. 118–134, July 2016. DOI: https://doi.org/10.1016/j.cmpb.2016.03.016

M. E. Haas et al., "Machine learning enables new insights into genetic contributions to liver fat accumulation," Cell Genomics, vol. 1, no. 3, Dec. 2021. DOI: https://doi.org/10.1016/j.xgen.2021.100066

Y. S. Park, Y. J. Moon, I. G. Jun, J. G. Song, and G. S. Hwang, "Application of the Revised Cardiac Risk Index to the Model for End-Stage Liver Disease Score Improves the Prediction of Cardiac Events in Patients Undergoing Liver Transplantation," Transplantation Proceedings, vol. 50, no. 4, pp. 1108–1113, May 2018. DOI: https://doi.org/10.1016/j.transproceed.2018.01.024

R. Masuzaki et al., "Noninvasive Assessment of Liver Fibrosis: Current and Future Clinical and Molecular Perspectives," International Journal of Molecular Sciences, vol. 21, no. 14, July 2020. DOI: https://doi.org/10.3390/ijms21144906

J. Singh, S. Bagga, and R. Kaur, "Software-based Prediction of Liver Disease with Feature Selection and Classification Techniques," Procedia Computer Science, vol. 167, pp. 1970–1980, Jan. 2020. DOI: https://doi.org/10.1016/j.procs.2020.03.226

A. A. Almelibari, M. I. Labib, and Y. Ramadan, "Enhancing Liver Disease Classification Based on a Stacked Machine Learning Model," Engineering, Technology & Applied Science Research, vol. 15, no. 5, pp. 26403–26409, Oct. 2025. DOI: https://doi.org/10.48084/etasr.11526

H. U. Janjua, F. Andleeb, S. Aftab, F. Hussain, and G. Gilanie, "Classification of Liver Cirrhosis with Statistical Analysis of Texture Parameters," International Journal of Optical Sciences, vol. 3, no. 2, pp. 18–25, 2017.

M. J. Kalita et al., "Vitamin-d receptor (VDR) polymorphism and types of HBV related liver disease along with an SVM based disease prediction model," Human Gene, vol. 37, Sept. 2023, Art. no. 201211. DOI: https://doi.org/10.1016/j.humgen.2023.201211

S. M. Ganie and P. K. Dutta Pramanik, "A comparative analysis of boosting algorithms for chronic liver disease prediction," Healthcare Analytics, vol. 5, June 2024, Art. no. 100313. DOI: https://doi.org/10.1016/j.health.2024.100313

J. Wang, Z. Qin, J. Hsu, and B. Zhou, "A fusion of machine learning algorithms and traditional statistical forecasting models for analyzing American healthcare expenditure," Healthcare Analytics, vol. 5, June 2024, Art. no. 100312. DOI: https://doi.org/10.1016/j.health.2024.100312

M. Zini and C. Carcasci, "Machine learning-based energy monitoring method applied to the HVAC systems electricity demand of an Italian healthcare facility," Smart Energy, vol. 14, May 2024, Art. no. 100137. DOI: https://doi.org/10.1016/j.segy.2024.100137

A. Yeganeh, A. Johannssen, N. Chukhrova, and M. Rasouli, "Monitoring multistage healthcare processes using state space models and a machine learning based framework," Artificial Intelligence in Medicine, vol. 151, May 2024, Art. no. 102826. DOI: https://doi.org/10.1016/j.artmed.2024.102826

A. A. Ahad, B. Das, M. R. Khan, N. Saha, A. Zahid, and M. Ahmad, "Multiclass liver disease prediction with adaptive data preprocessing and ensemble modeling," Results in Engineering, vol. 22, June 2024, Art. no. 102059. DOI: https://doi.org/10.1016/j.rineng.2024.102059

M. Ibraigheeth, "Software project risk assessment using machine learning approaches," American Journal of Multidisciplinary Research & Development, vol. 4, no. 2, pp. 35–41, Feb. 2022.

M. A. Ibraigheeth, A. I. A. Eid, Y. A. Alsariera, W. F. Awwad, and M. Nawaz, “A New Weighted Ensemble Model to Improve the Performance of Software Project Failure Prediction,” International Journal of Advanced Computer Science and Applications, vol. 15, no. 2, 2024. DOI: https://doi.org/10.14569/IJACSA.2024.0150238

"Liver Disease Patient Dataset 30K train data." Kaggle, [Online]. Available: https://www.kaggle.com/datasets/abhi8923shriv/liver-disease-patient-dataset.