Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 11 , Issue 1 , PP: 29-57, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Khaled Sh. Gaber 1 * , Amal H. Alharbi 2
Doi: https://doi.org/10.54216/JAIM.110102
The growing heterogeneity of cardiovascular disease presentations poses significant c hallenges for clinical decision support systems, particularly in identifying patient similarities and developing robust predictive models capable of supporting personalized treatment strategies, which motivates the need for advanced data-driven frameworks that can jointly exploit unsupervised learning, deep learning, and intelligent optimization. In this study, we propose a comprehensive hybrid framework that integrates unsupervised patient clustering with deep learning classification, enhanced through Fitness Greylag Goose Optimization (FGGO), where clustering is first employed to uncover latent patient subgroups and inform downstream learning, followed by the use of a Deep Learning Framework Distilled by Gradient Boosting Decision Trees (DeepGBM) as the core predictive model, and finally optimized via FGGO for automated hyperparameter tuning. The primary contribution of this work lies in the design of an FGGO-optimized DeepGBM framework that systematically improves learning stability, feature interaction modeling, and predictive robustness, while also providing a rigorous comparative evaluation against other state-of-the-art metaheuristic optimizers, including Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Dipper Throated Optimization (DDTO), and Multiverse Optimization (MVO). Experimental results demonstrate that, at the baseline stage without optimization, DeepGBM achieves an accuracy of 0.9032, sensitivity of 0.8824, specificity of 0.9195, and F-score of 0.8889, indicating strong but improvable performance on heart disease patient data. After metaheuristic optimization, the proposed FGGO + DeepGBM model exhibits a substantial performance enhancement, reaching an accuracy of 0.9795, sensitivity of 0.9747, specificity of 0.9831, positive predictive value of 0.9776, negative predictive value of 0.9809, and an F-score of 0.9761, consistently outperforming PSO + DeepGBM, GWO + DeepGBM, DDTO + DeepGBM, and MVO + DeepGBM across all evaluation metrics. These results highlight the robustness and convergence consistency of FGGO-based optimization and confirm i ts e ffectiveness in navigating complex hyperparameter search spaces. The implications of this work extend to clinical practice and intelligent healthcare systems, as the proposed framework offers a reliable and scalable solution for patient stratification and heart disease prediction, supporting more accurate, interpretable, and data-driven clinical decision-making while paving the way for future integration into personalized and precision medicine applications.
Heart disease prediction , Patient clustering , Deep learning optimization , Metaheuristic algorithms , Clinical decision support systems
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