A label-enhanced graph neural network optimized by Elk Herd optimization for heart disease detection

Early detection of heart disease is essential for minimizing mortality, particularly in resource-constrained health-care environments. Recent models have shown accurate performance for medical diagnosis; however, they often struggles from limited interpretability and sensitivity to missing clinical data. To address these challenges, this paper proposes A Label-Enhanced Graph Neural Network Optimized by Elk Herd Optimization for Heart Disease Detection (HDD–LEGNN–EHOA). The input data is taken from Cleveland Heart disease data collection. Initially, data are pre-processed using Generalized Correntropy Sparse Gauss–Hermite Quadrature Filter (GCSGQF) to impute missing values. Then, the processed data is given to Secretary Bird Optimization Algorithm (SBOA) to select optimal characteristics. Then, the selected attributes fed to Label-Enhanced Graph Neural Network (LEGNN) to detect heart disease as heart disease present and heart disease absent. Elk Herd Optimization Algorithm (EHOA) to fine-tune the weight parameter of LEGNN. The newly suggested HDD–LEGNN–EHOA framework is assessed based on some metrics like accuracy, sensitivity, and computational time. Finally, the performance of HDD–LEGNN–EHOA method provides attains 25.68%, 22.54%, and 31.24% higher accuracy when compared to the existing models, respectively.