Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Savitha, S.; Rajiv Kannan, A.; Logeswaran, K. Title A name given to the resource Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Biomedical Signal Processing and Control;Volume;117;Issue;;Article No.;109606; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.bspc.2026.109606" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.bspc.2026.109606</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105027733541?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105027733541?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Savitha S., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Rajiv Kannan A., Department of CSE, K.S.R. College of Engineering, Tamil Nadu, Tiruchengode, 637215, India; Logeswaran K., Department of AI and Data Science Engineering, School of Engineering and Technology, CHRIST University, Kengeri, Karnataka, Bangalore, 560074, India Description An account of the resource The study addresses the critical challenge of accurately predicting cardiovascular disease (CVD), a leading cause of mortality worldwide, where early diagnosis is crucial for effective intervention. Traditional models often struggle with high-dimensional data, imbalanced classes, and nonlinear feature interactions, limiting prediction reliability. Motivated by these gaps, this research proposes a hybrid methodology integrating Harris Hawks Search (HHS) for feature optimization with Radial Basis Function Networks (RBFN) to enhance CVD risk assessment. The HHS algorithm efficiently selects key predictive features such as chest pain type and number of vessels, reducing dimensionality while preserving vital information. Trained on optimized features, the RBFN classifier achieved superior performance with 92.1% accuracy, high sensitivity, and specificity, surpassing conventional models like Logistic Regression (81.2%) and Random Forest (86.7%). Ablation studies confirm each component's contribution, with significant gains validated statistically (p &lt; 0.05). The hybrid model also offers computational efficiency with training times around 31.7 s. Future work aims to validate this approach on diverse, larger datasets and integrate it into real-time clinical decision support systems, advancing personalized, interpretable, and efficient cardiovascular healthcare tools. 2026 Elsevier Ltd Subject The topic of the resource Cardiovascular disease prediction; Harris Hawks Search; Hybrid feature optimization; Metaheuristic feature selection and machine learning; Radial basis function networks Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 17468094; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online