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Consequently, bio\u2010inspired algorithms have led to several impactful applications in heart\u2010related ailments, demonstrating significant effectiveness in this domain. The Harris\u2019 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\u2010Objective hybrid Harris Hawks Optimization (MO\u2010hHHO) algorithm, a novel enhancement of the HHO. Designed to address complex multiobjective problems (MOPs), MO\u2010hHHO 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\u2010nearest 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\u2010objective problems and MOPs reveal that the MO\u2010hHHO effectively reduces the number of features while optimizing classifier hyperparameters, achieving superior classification accuracy, particularly for RF, LR, and SVM. 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