Comparative Analysis of Cardiac Disease Classification Using a Deep Learning Model Embedded with a Bio-Inspired Algorithm

Cardiac disease classification is a crucial task in healthcare aimed at early diagnosis and prevention of cardiovascular complications. Traditional methods such as machine learning models often face challenges in handling high-dimensional and noisy datasets, as well as in optimizing model performance. In this study, we propose and compare a novel approach for heart disease prediction using deep learning models embedded in bioinspired algorithms. The integration of deep learning techniques allows for automatic feature learning and complex pattern recognition from raw data, while bioinspired algorithms provide optimization capabilities for enhancing model accuracy and generalization. Specifically, the cuckoo search algorithm and elephant herding optimization algorithm are employed to optimize the architecture and hyperparameters of deep learning models, facilitating the exploration of diverse model configurations and parameter settings. This hybrid approach enables the development of highly effective predictive models by efficiently leveraging the complementary strengths of deep learning and bioinspired optimization. Experimental results on benchmark heart disease datasets demonstrate the superior performance of the proposed method compared to conventional approaches, achieving higher accuracy and robustness in predicting heart disease risk. The proposed framework holds significant promise for advancing the state-of-the-art in heart disease prediction and facilitating personalized healthcare interventions for at-risk individuals.