MO‐hHHO: Multi‐Objective Hybrid Harris Hawks Optimization for Prediction of Coronary Artery Disease

Coronary artery disease is a major cause of mortality and morbidity worldwide, underscoring the need for accurate diagnosis to ensure effective treatment. Consequently, bio‐inspired algorithms have led to several impactful applications in heart‐related ailments, demonstrating significant effectiveness in this domain. The Harris’ Hawks Optimization (HHO) is a recent metaheuristic algorithm inspired by the cooperative behavior of the hawks. This article presents, for the first time, the Multi‐Objective hybrid Harris Hawks Optimization (MO‐hHHO) algorithm, a novel enhancement of the HHO. Designed to address complex multiobjective problems (MOPs), MO‐hHHO focuses on feature selection minimization and classifier hyperparameter optimization, achieving improved predictive accuracy. To assess the performance of the proposed algorithm, four different machine learning classifiers are used: random forest (RF), K‐nearest neighbors, logistic regression (LR), and support vector machines (SVM). Extensive experiments are carried out using heart disease dataset obtained from the Kaggle repository. The experimental results on both single‐objective problems and MOPs reveal that the MO‐hHHO effectively reduces the number of features while optimizing classifier hyperparameters, achieving superior classification accuracy, particularly for RF, LR, and SVM. Additionally, hybrid HHOs adaptive exploration and exploitation mechanisms ensure efficient convergence toward optimal solutions, making it well‐suited for high‐dimensional and complex optimization tasks.