Key result
Novel deep learning model classifies heart disease with ~97% accuracy, outperforming Random Forest.
Why the study?
Conventional machine learning approaches often struggle to handle high-dimensional medical data and identify the most relevant diagnostic features, affecting heart disease prediction reliability.
Does the IFRM-HDP deep learning model improve heart disease classification accuracy compared to existing machine learning models in a benchmark clinical dataset?
Does the IFRM-HDP deep learning model improve heart disease classification accuracy compared to existing machine learning models in a benchmark clinical dataset?
A hybrid deep learning framework combining Boruta and mRMR feature selection with a Temporal Convolutional Network achieved 97.40% accuracy in classifying heart disease from a benchmark clinical dataset.
May aid AI-based heart disease detection research; leaves open prospective clinical validation before practice adoption.
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.
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Mohammed Assiri (2026) studied Heart Disease (n=1,025). Integrating Feature Reduction Models for Heart Disease Prediction (IFRM-HDP) combining Boruta, mRMR, and Temporal Convolutional Network (TCN) vs. Existing machine learning and deep learning models (e.g., Random Forest, Soft voting ensemble, XGBoost) was evaluated on Heart disease classification accuracy on testing dataset (30% split). The proposed IFRM-HDP deep learning model achieved 97.40% accuracy, precision, recall, F1-score, and AUC in classifying heart disease, outperforming Random Forest (93.80% accuracy).
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