Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction

Title

Hybrid feature optimization and radial basis function networks for cardiovascular disease prediction

Creator

Savitha, S.; Rajiv Kannan, A.; Logeswaran, K.

Description

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 < 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

Source

Biomedical Signal Processing and Control;Volume;117;Issue;;Article No.;109606;

Date

01-01-2026

Publisher

Elsevier Ltd

Subject

Cardiovascular disease prediction; Harris Hawks Search; Hybrid feature optimization; Metaheuristic feature selection and machine learning; Radial basis function networks

Coverage

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

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 17468094;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.1016/j.bspc.2026.109606

https://www.scopus.com/pages/publications/105027733541?origin=resultslist