Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 11 , Issue 1 , PP: 87-116, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Benyamin Abdollahzadeh 1 * , Marwa M. Eid 2
Doi: https://doi.org/10.54216/JAIM.110104
The rapid increase in liver disease prevalence worldwide, particularly in developing regions, necessitates accurate and reliable diagnostic systems capable of supporting early clinical decision-making based on routine laboratory data. Traditional diagnostic approaches and unoptimized machine learning models often struggle to fully capture the complex, nonlinear relationships among biochemical liver indicators, leading to suboptimal predictive reliability. Motivated by these challenges, this study proposes a human-inspired metaheuristic optimization framework that integrates the iHow Optimization Algorithm (iHOW) with the Extreme Gradient Boosting model (XGBoost) to enhance liver disease prediction performance. The main contribution of this work lies in the development of an optimized diagnostic pipeline that systematically tunes XGBoost hyperparameters using iHOW and rigorously benchmarks its effectiveness against established metaheuristic optimizers, including Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (GWO), and Greylag Goose Optimization (GGO). Experimental evaluation is conducted on a clinically sourced liver disease dataset using multiple diagnostic metrics. In the baseline stage, the unoptimized XGBoost model achieves an accuracy of 0.921875, sensitivity of 0.920245399, specificity of 0.923566879, and F-Score of 0.923076923. After hyperparameter optimization, the proposed iHOW+XGBoost framework demonstrates substantial performance enhancement, attaining an accuracy of 0.983696458, sensitivity of 0.983391608, specificity of 0.984012066, and F-Score of 0.983965015, outperforming GA+XGBoost, PSO+XGBoost, GWO+XGBoost, and GGO+XGBoost across all evaluated metrics. These results confirm the effectiveness of human-inspired optimization in navigating complex hyperparameter search spaces and improving diagnostic robustness. The findings of this study highlight the practical implications of integrating advanced metaheuristic optimization with ensemble learning models, offering a highly accurate, reliable, and scalable decision-support framework that can be leveraged for early liver disease screening and extended to other medical diagnostic and predictive healthcare applications.
Liver disease prediction , Machine learning in healthcare , Metaheuristic optimization , Hyperparameter tuning , Clinical decision support systems
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