An Intelligent Metaheuristic-Optimized Deep Learning
Approach for Heart Disease Diagnosis and Patient
Stratification
Khaled Sh. Gaber1,*, Amal H. Alharbi2
1Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
2Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah
bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Emails: khsherif@jcsis.org; ahalharbi@pnu.edu.sa
Abstract
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.
Keywords: Heart disease prediction; Patient clustering; Deep learning optimization; Metaheuristic
algorithms; Clinical decision support systems