A Human-Inspired Metaheuristic Optimization Framework for
Accurate Liver Disease Prediction Using Clinical Laboratory
Data
Benyamin Abdollahzadeh1,*, Marwa M. Eid2,3
1Department of Mathematics, Faculty of Science, University of Hradec Králové, 500 03, Hradec
Králové, Czech Republic
2Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 11152, Egypt
3Jadara Research Center, Jadara University, Irbid 21110, Jordan
Emails: benyamin.abdolahzade@gmail.com; mmm@ieee.org
Abstract
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.
Keywords: Liver disease prediction; Machine learning in healthcare; Metaheuristic optimization;
Hyperparameter tuning; Clinical decision support systems