Heart Disease Prediction Model Using Feature Selection and Ensemble Deep Learning with Optimized Weight - Al-Mahdi, Iman S.; Darwish, Saad M.; Madbouly, Magda M. (Computer modeling in engineering & sciences, 2025)
Heart disease prediction is a critical issue in healthcare, where accurate early diagnosis can save lives and reduce healthcare costs. The problem is inherently complex due to the high dimensionality of medical data, irrelevant or redundant features, and the variability in risk factors such as age, lifestyle, and medical history. These challenges often lead to inefficient and less accurate models. Traditional prediction methodologies face limitations in effectively handling large feature sets and optimizing classification performance, which can result in overfitting poor generalization, and high computational cost. This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm (GA) with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm (TSA). GA selects the most relevant features, reducing dimensionality and improving model efficiency. The selected features are then used to train an ensemble of deep learning models, where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy. This hybrid approach addresses key challenges in the field, such as high dimensionality, redundant features, and classification performance, by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble. These enhancements result in a model that achieves superior accuracy, generalization, and efficiency compared to traditional methods. The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditional models. Specifically, it achieved an accuracy of 97.5%, a sensitivity of 97.2%, and a specificity of 97.8%. Additionally, with a 60–40 data split and 5-fold cross-validation, the model showed a significant reduction in training time (90 s), memory consumption (950 MB), and CPU usage (80%), highlighting its effectiveness in processing large, complex medical datasets for heart disease prediction.