Heart disease prediction using rough neutrosophic sets and dual-attention neural networks: RNS-OptiDANet

Introduction: Heart disease is a major global health problem that highlights the need for effective and accurate prediction methods. Methods: This paper presents RNS-OptiDANet, a hybrid framework that combines rough set theory (RST), rough neutrosophic sets (RNS) and an optimized dual-attention neural network (OptiDANet) in order to predict heart disease. For feature selection, the QuickReduct method with the discernibility matrix (RST QRDM) was used. The features selected in RST were represented as RNS representations to deal with uncertainty in the classification process. The OptiDANet model implements Dual Attention Mechanisms such as Channel Attention (CAM) and Soft Attention Mechanism (SAM) to highlight the relevant patterns while reducing noise. The performance of the developed framework has been improved through Hyperparameter tuning using Optuna and overfitting has been avoided. Finally, classification is conducted using a Random Forest (RF) model. Results: Experimental results demonstrate strong performance in terms of accuracy, precision, recall and F1-score across datasets. Discussion: An eXplainable Artificial Intelligence (XAI) module is integrated to provide feature level interpretability and clinical transparency while ablation study validates the contribution of each framework component confirming the robustness and effectiveness of the proposed hybrid RNS-OptiDANet model.