Accurate feature selection remains a critical challenge in heart disease prediction due to high‐dimensional data and complex feature interactions. This study proposes a hybrid framework that integrates extreme gradient boosting with a chaotic binary bat algorithm to improve feature‐selection efficiency and predictive performance. The proposed approach incorporates chaotic dynamics to enhance search stability and reduce premature convergence in binary optimization. Experimental results on two benchmark cardiovascular datasets demonstrate that the proposed model consistently outperforms conventional feature‐selection methods and metaheuristic algorithms, including genetic algorithm and particle swarm optimization. On Dataset I, the model achieves a test accuracy of 0.917 ± 0.005 and an F1‐score of 0.926 ± 0.004, representing an improvement of approximately 3.3%–3.4% over baseline extreme gradient boosting. On Dataset II, both the proposed method and the binary bat algorithm achieve identical optimal performance (F1‐score = 1.000), indicating high dataset separability. Furthermore, the proposed approach reduces computational time by up to 2.5 times compared to the genetic algorithm and particle swarm optimization. The findings indicate that feature selection is the primary factor driving performance improvement, while chaotic dynamics mainly enhance convergence efficiency and stability. This study contributes a robust and efficient feature‐selection framework that effectively balances accuracy, computational cost, and interpretability for heart disease prediction.
Anam et al. (Thu,) studied this question.