ENHANCED LIVER DISEASE CLASSIFICATION USING ICA-CSO OPTIMIZED SVM MODEL
Keywords:
Liver disease detection, machine learning, Independent Component Analysis, Crow Search Optimization algorithmAbstract
Liver disease (LD) is a serious worldwide medical issue, which requires the development of precise as well as effective ways to diagnose that will improve early detection and treatment results. This paper put forward an enhanced Machine Learning (ML) approach for LD recognition with the aim to improve assessment efficiency. The suggested approach makes use of data preparation approaches as to improve feature extraction through the use of the Independent Component Analysis and Crow Search Optimization algorithm (ICA+CSOA) and an Improved Support Vector Machine (ISVM) classifier. Essential indicators including accuracy, precision, recall, and Mean Squared Error (MSE) are used to assess and compare the model's performance with other methods that are already in use such as Random Forest (RF), Support Vector Machines (SVM), Neural Networks (NN), and Logistic Regression (LR). The introduced model outperforms all other strategies with an accuracy of 95.63% and an MSE of 4.37%. According to these results, the suggested approach offers a very dependable way to identify liver illness, which may help medical experts.
References
J. Allenki and H. Kumar Soni, “Analysis of chronic liver disease detection by using machine learning techniques,” in 2024 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), 2024, pp. 1–8.
S. M. Ganie, P. K. Dutta Pramanik, and Z. Zhao, “Improved liver disease prediction from clinical data through an evaluation of ensemble learning approaches,” BMC Med. Inform. Decis. Mak., vol. 24, no. 1, p. 160, 2024.
M. Hassan Mohamed et al., “Toward an accurate liver disease prediction based on two-level ensemble stacking model,” IEEE Access, vol. 12, pp. 180210–180237, 2024.
S. Tokala et al., “Liver disease prediction and classification using machine learning techniques,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 2, 2023.
A. S. Rahman, F. J. M. Shamrat, Z. Tasnim, J. Roy, and S. A. Hossain, “A comparative study on liver disease prediction using supervised machine learning algorithms,” International Journal of Scientific & Technology Research, vol. 8, no. 11, pp. 419–422, 2019.
S. Shaprapawad and P. Borugadda, “A novel approach for prediction of liver disease using voting algorithm based on machine learning models,” in Smart Innovation, Systems and Technologies, Singapore: Springer Nature Singapore, 2024, pp. 1–13.
J. Singh, S. Bagga, and R. Kaur, “Software-based prediction of liver disease with feature selection and classification techniques,” Procedia Comput. Sci., vol. 167, pp. 1970–1980, 2020.
J. H. Joloudari, H. Saadatfar, A. Dehzangi, and S. Shamshirband, “Computer-aided decision-making for predicting liver disease using PSO-based optimized SVM with feature selection,” Inform. Med. Unlocked, vol. 17, no. 100255, p. 100255, 2019.
K. Gupta, N. Jiwani, N. Afreen, and Divyarani, “Liver Disease Prediction using Machine learning Classification Techniques,” in 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), 2022.
N. Arora, S. Srivastava, A. Tripathi, and V. Gupta, “An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 37, no. 1, pp. 214–222, 2025.
M. Ghosh et al., “A comparative analysis of machine learning algorithms to predict liver disease,” Intell. Autom. Soft Comput., vol. 30, no. 3, pp. 917–928, 2021.
S. Afrin et al., “Supervised machine learning based liver disease prediction approach with LASSO feature selection,” Bull. Electr. Eng. Inform., vol. 10, no. 6, pp. 3369–3376, 2021.
S. H. Rizvi, A. Mishra, S. K. Singh, C. K. Jain, and S. Kumar, Comparative Analysis of Various Algorithms on Multi-Diseases Using Machine Learning.
Z. Wang, F. Y. Li, J. Cai, Z. Xue, Y. Zhou, and Z. Wang, “Construction and validation of a machine learning-based prediction model for short-term mortality in critically ill patients with liver cirrhosis,” Clin. Res. Hepatol. Gastroenterol., vol. 49, no. 1, p. 102507, 2025.
M. H. Mohamed et al., “Towards an Accurate Liver Disease Prediction Based on Two-level Ensemble Stacking Model,” IEEE Access, 2024.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
