Harmonizing Machine Learning Algorithms for Enhanced ‎Liver Disease Prediction: A Comparative Study of Accuracy and Efficacy

  • Authors

    • Laxminath Tripathy Faculty of Engineering & Technology, ITER, Siksha ‘O’ Anusandhan University. Odisha, India
    • Dharmveer Kumar Yadav Katihar Engineering College, Katihar, Science, Technology and Technical Education Department, Govt. of Bihar, India
    • Brijesh Kumar Singh Govt. Engineering College, Jehanabad, Science, Technology and Technical Education Department, Govt. of Bihar, India
    https://doi.org/10.14419/vg1c4b90

    Received date: April 26, 2025

    Accepted date: June 27, 2025

    Published date: August 19, 2025

  • Liver Disease; Machine Learning; Classification; Feature Selection; Ensemble Techniques; Support Vector Machines; Random For‎Est; Logistic Regres-sion.

  • Abstract

    In this review paper, various machine learning techniques applied to predict liver disease have been studied. Non-Alcoholic ‎Fatty Liver Disease (NAFLD) is a leading cause of global mortality, which affects millions of people worldwide. The diagnosis ‎of NAFLD can be both costly and complex. Therefore, this research aims to reduce these challenges by assessing the capability ‎of various machine learning models to prevent NAFLD, thereby reducing the overall cost of diagnosis. We have used 3 classification algorithms-‎Logistic Regression, Support Vector Machine, along the Random Forest technique for enhanced prediction accuracy. ‎Our study evaluates these models based on metrics like accuracy, precision, recall, and F1 score. Also, we are inclined towards ‎developing a hybrid model to further improve efficiency. Based on our study of various other papers, it was found that integrating-‎ing multiple Machine Learning (ML) techniques helps to significantly enhance the performance, offering an efficient approach ‎towards the diagnosis‎.

  • References

    1. Gogi, V. J., & M. N., V. (2018). Prognosis of liver disease: Using machine learning algorithms. 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE), 875–879. https://doi.org/10.1109/ICRIEECE44171.2018.9008482.
    2. Gupta, K., Jiwani, N., Afreen, N., & D, D. (2022). Liver disease prediction using ML classification techniques. 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 221–226. https://doi.org/10.1109/CSNT54456.2022.9787574.
    3. Rahman, A. K. M. S., Shamrat, F. M. J. M., Tasnim, Z., Roy, J., & Hossain, S. A. (2019). A comparative study on liver disease prediction using supervised machine learning algorithms. International Journal of Scientific & Technology Research, 8(11), 419–422.
    4. Terlapu, P., Sadi, R., Pondreti, R., & Tippana, C. (2022). Intelligent identification of liver diseases based on an incremental hidden layer neurons ANN model. International Journal of Computing and Digital Systems, 11(1), 1027–1050. https://doi.org/10.12785/ijcds/110183.
    5. Yadav, E. S., Bachepalli, S., & Prasanna, K. (2023). Liver disease prediction using machine learning. International Journal of Advance Research and Innovative Ideas in Education, 9(2), 65–69.
    6. Wu, C.-C., Yeh, W.-C., Hsu, W.-D., Islam, M. M., Nguyen, P. A., Poly, T. N., Wang, Y.-C., Yang, H.-C., & Li, Y.-C. (2019). Prediction of fatty liver disease using machine learning algorithms. Computer Methods and Programs in Biomedicine, 170, 23–29. https://doi.org/10.1016/j.cmpb.2018.12.032.
    7. Amin, R., Yasmin, R., Ruhi, S., Rahman, M. H., & Reza, M. S. (2023). Prediction of chronic liver disease patients using integrated projection-based statistical feature extraction with machine learning algorithms. Informatics in Medicine Unlocked, 36, 101155. https://doi.org/10.1016/j.imu.2022.101155.
    8. Spann, A., Yasodhara, A., Kang, J., Watt, K., Wang, B., Goldenberg, A., & Bhat, M. (2020). Applying machine learning in liver disease and transplantation: A comprehensive review. Hepatology, 71(3), 1093–1105. https://doi.org/10.1002/hep.31103.
    9. El-Shafeiy, E., El-Desouky, A., & Elghamrawy, S. (2018). Prediction of liver diseases based on machine learning technique for big data. In Advanced Machine Learning Technologies and Applications (AMLTA2018) (Vol. 723). Springer. https://doi.org/10.1007/978-3-319-74690-6_36.
    10. Razmpour, F., Daryabeygi-Khotbehsara, R., Soleimani, D., & others. (2023). Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices. Scientific Reports, 13, 4942. https://doi.org/10.1038/s41598-023-32129-y.
    11. Ahn, J. C., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V. H. (2021). Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology, 73(6), 2546–2563. https://doi.org/10.1002/hep.31603.
    12. Ghosh, M., Raihan, M. M. S., Raihan, M., Akter, L., Bairagi, A. K., Alshamrani, S. S., & Masud, M. (2021). A comparative analysis of machine learning algorithms to predict liver disease. Intelligent Automation & Soft Computing, 30(3), 917–928. https://doi.org/10.32604/iasc.2021.017989.
    13. Aslam, M. H., Hussain, S. F., & Ali, R. H. (2022). Predictive analysis on severity of non-alcoholic fatty liver disease (NAFLD) using machine learning algorithms. 17th International Conference on Emerging Technologies (ICET), 95–100. https://doi.org/10.1109/ICET56601.2022.10004660.
    14. Liu, Y., Liu, X., Cen, C., Li, X., Liu, J., Ming, Z., Yu, S., Tang, X., Zhou, L., Yu, J., Huang, K., & Zheng, S. (2021). Comparison and development of advanced machine learning tools to predict non-alcoholic fatty liver disease: An extended study. Hepatobiliary & Pancreatic Diseases International, 20(5), 409–415. https://doi.org/10.1016/j.hbpd.2021.08.004.
    15. Perveen, S., Shahbaz, M., Keshavjee, K., & others. (2018). A systematic machine learning-based approach for the diagnosis of non-alcoholic fatty liver disease risk and progression. Scientific Reports, 8, 2112. https://doi.org/10.1038/s41598-018-20166-x.
    16. Ghazal, T. M., Rehman, A. U., Saleem, M., Ahmad, M., Ahmad, S., & Mehmood, F. (2022). Intelligent model to predict early liver disease using machine learning technique. International Conference on Business Analytics for Technology and Security (ICBATS), 1–5. https://doi.org/10.1109/ICBATS54253.2022.9758929.
    17. Sadhasivam, J., J, S., RM, G., & Chellapan, N. (2021). Liver disease prediction using machine learning classification. Webology, 18(2), 441–452. https://doi.org/10.14704/WEB/V18SI02/WEB18293.
    18. Zhou, L. Q., Wang, J. Y., Yu, S. Y., Wu, G. G., Wei, Q., Deng, Y. B., Wu, X. L., Cui, X. W., & Dietrich, C. F. (2019). Artificial intelligence in medical imaging of the liver. World Journal of Gastroenterology, 25(6), 672–682. https://doi.org/10.3748/wjg.v25.i6.672.
    19. Md, A. Q., Kulkarni, S., Joshua, C. J., Vaichole, T., Mohan, S., & Iwendi, C. (2023). Enhanced preprocessing approach using ensemble machine learning algorithms for detecting liver disease. Biomedicines, 11, 581. https://doi.org/10.3390/biomedicines11020581.
    20. Mostafa, F., Hasan, E., Williamson, M., & Brock, M. N. (2021). Statistical ML approaches to liver disease prediction. Livers, 1(4), 294–312. https://doi.org/10.3390/livers1040023.
    21. Priya, M. B., Juliet, P. L., & Tamilselvi, P. R. (2018). Performance analysis of liver disease prediction using machine learning algorithms. International Research Journal of Engineering and Technology, 5(1), 206–211.
    22. Dristas, E., & Trigka, M. (2023). Supervised ML models for liver disease risk prediction. Computers, 12(1), 19. https://doi.org/10.3390/computers12010019.
    23. Khan, B., Naseem, R., Ali, M., Arshad, M., & Jan, N. (2019, November 23). Machine learning approaches for liver disease diagnosing. International Journal of Data Science and Analytics Applications. https://ijdsaa.com/index.php/ welcome/article/ view/71. https://doi.org/10.69511/ijdsaa.v1i1.71.
    24. Rani, S., & Dey, N. (2022). Machine learning in healthcare: Challenges and opportunities. In N. Dey, A. S. Ashour, & S. Balas (Eds.), Machine Learning in Biomedical Science (Chap. 18). Wiley. https://doi.org/10.1002/9781119785620.ch18.
    25. Ramana, B., Surendra, M., Prasad Babu, G., & Nagasuri, B. V. (2012). A critical comparative study of liver patients from USA and India: An exploratory analysis. International Journal of Computer Science, 9.
    26. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
    27. Rajkomar, A., Dean, J., & Kohane, I. (2018). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259.

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  • How to Cite

    Tripathy, L., Yadav , D. K. ., & Singh , B. K. . (2025). Harmonizing Machine Learning Algorithms for Enhanced ‎Liver Disease Prediction: A Comparative Study of Accuracy and Efficacy. International Journal of Basic and Applied Sciences, 14(4), 522-532. https://doi.org/10.14419/vg1c4b90