A bio-inspired approach to feature optimization for ischemic heart disease detection

Ischemic Heart Disease (IHD) stands as one of the primary contributors to worldwide deaths, therefore requiring precise and efficient predictive models. Standard machine learning techniques encounter hurdles, including excessive feature dimensions and unbalanced data distribution together with inappropriate feature group choice that negatively affect model effectiveness. The research introduces an optimized feature selection method by employing an Improved Squirrel Search Algorithm (ISSA) to raise the predictive capacity for IHD classification. The ISSA implements adaptive search features to automatically optimize feature selection, through which it maintains important attributes while eliminating redundant information. The selected features are evaluated using a Random Forest classifier, known for its robustness and interpretability in medical prediction tasks. Experimental results on the University of California Irvine (UCI) Heart Disease dataset show that the Improved Squirrel Search Algorithm–Random Forest (ISSA-RF) model achieves a classification accuracy of 98.12%, outperforming existing feature selection techniques while reducing computational overhead. Bio-inspired optimization proves effective in medical diagnostics through recent research findings that lead to more efficient predictive healthcare models with interpretable properties. • Introduce a bio-inspired optimization method for improved feature selection in heart disease prediction. • Apply adaptive learning to eliminate redundant data and enhance diagnostic accuracy. • Integrate intelligent search with ensemble learning for efficient healthcare analytics. • Demonstrate improved model interpretability with reduced computational cost. • Validate a dynamic optimization strategy that enhances reliability in clinical decision support.