Illustration of the hyperplane and margin of the modified SVM model: The figure shows how SVM develops the optimal hyperplane to separate two classes to maximize the margin.

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• Pseudocode for the modified Support Vector Machine (SVM) classifier: It highlights the particular changes that were made to improve the SVM to forecast cardiovascular risks.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• Pseudocode for the modified Particle Swarm Optimization (PSO) algorithm used for SVM hyperparameter tuning: The figure gives the iterative optimization of SVM hyperparameters with PSO.
  by: Medhat A. Tawfeek (22522087)
  Published: (2025)
• Pseudocode for the AI interpretability module using SHapley Additive exPlanations (SHAP): The module is fed with the optimized SVM, and a set of input features to a particular prediction.
  by: Medhat A. Tawfeek (22522087)
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• Schematic architecture of the proposed hybrid SVM-PSO-AI framework for cardiovascular risk prediction: It has four input vectors V1 (Medical Imaging), V2 (Clinical Data), V3 (Genomic Information) and V4 (Medical History).
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  Published: (2025)
• Ablation study quantifying the individual contribution of each core component to the performance of the hybrid framework. The study systematically evaluates three configurations: a baseline Standard SVM without hyperparameter optimization, the SVM optimized with the proposed modified Particle Swarm Optimization (PSO) algorithm and the Full Hybrid Model. The findings indicate that the PSO optimization can be used to gain a significant performance improvement in all metrics (e.g., + 4.4% accuracy, + 3.5% AUC) which proves the importance of the pivot role of hyperparameter tuning. Incorporating the SHAP module that introduces explainability does not lead to degradation of performance, as it proves that the framework provides high accuracy without compromising interpretability, which is one of the conditions that must be fulfilled when adopting the framework by a healthcare institution.
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  Published: (2025)