A Deep Learning‐Based <scp>DenseEchoNet</scp> Framework With <scp>eXplainable</scp> Artificial Intelligence for Accurate and Early Heart Disease Prediction

ABSTRACT Heart disease (HD) is still a major cause of death worldwide, which emphasizes the importance of early and precise prediction. This paper presents DenseEchoNet, a deep learning model that has been optimized with the Gazelle Optimizer Algorithm (GOA). The hybrid HD‐ENN technique is used for balanced learning to solve class imbalance and high dimensionality, while squared exponential kernel‐based PCA (SEKPCA) effectively reduces dimensionality. DenseEchoNet outperforms current baseline models with accuracies of 0.9795 as well as 0.9785, respectively, when tested on the HDHI and Cleveland datasets. XAI approaches, such as LIME and SHAP, improve model interpretability by offering distinct insights on feature contributions to HD risk. For early HD prediction, this system provides a straightforward, accurate, and efficient solution.