A Hybrid Temporal Deep Learning-Based Intelligent Heart Disease Detection and Classification Model

Conventional Machine Learning (ML) approaches often struggle to handle high-dimensional medical data and to identify the most relevant diagnostic features, thereby affecting prediction reliability. To address these limitations, this study proposes an Artificial Intelligence (AI)-based model, namely Integrating Feature Reduction Models for Heart Disease Prediction (IFRM-HDP). The proposed model combines a hybrid feature selection strategy incorporating Boruta and minimum Redundancy Maximum Relevance (mRMR) with a Temporal Convolutional Network (TCN) to capture complex dependencies among clinical attributes and improve classification performance. By integrating feature relevance analysis with temporal pattern learning, the model improves prediction reliability while reducing the influence of redundant medical attributes. The Adam optimization algorithm is employed for adaptive parameter updating, enabling stable convergence and better generalization during model training. Experimental analysis is conducted on a benchmark Heart Disease (HD) dataset, and performance is evaluated using several assessment metrics. Comparative analysis indicates that the proposed model outperformed existing approaches, achieving an accuracy of 97.40%. Overall, the proposed model serves as an effective tool for intelligent HD prediction and clinical decision support applications.