Diet, stress, genetics, and a sedentary lifestyle may all contribute to heart disease rates. Although recent studies propose comprehensive automated diagnostic systems, these systems tend to focus on one aspect, such as feature selection, prioritization, or predictive accuracy. A more complete approach that considers all of these factors can improve the efficiency of a cardiac prediction system. This study uses an appropriate strategy to overcome potential network design problems, design challenges, overfitting, and lack of robustness that can interfere with system performance. The research introduces an ideally designed deep trust network called ID-DTN to improve system performance. The Ruzzo-Tompa method is used to eliminate noncontributory features. The Seagull Optimization Algorithm (SOA) is introduced to optimize the trust depth network to achieve optimal network design. The study scrutinizes the deep trust network (ID-DTN) and the restricted Boltzmann machine (RBM) and sheds light on the system's operation. This proposal can optimize both network architecture and feature selection, which is the main novelty. The proposed method is analyzed using the below-mentioned metrics: Matthew's correlation coefficient, F1 score, accuracy, sensitivity, specificity, and accuracy. ID-DTN performs well compared to other state-of-the-art methods. The validation results confirm that the proposed method improves the prediction accuracy to 97.11% and provides reliable recommendations for patients with cardiovascular disease.

Department of Cardiovascular Medicine The 5th People's Hospital of Ganzhou Ganzhou 341000 Jiangxi China

Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University 13713 Diriyah Riyadh Saudi Arabia

Department of Internal Medicine Xingguo County People's Hospital Ganzhou 341000 Jiangxi China

Department of Mathematics College of Science and Arts King Abdul Aziz University Rabigh Saudi Arabia

Department of Mathematics Faculty of Science University of Hradec Kralove 50003 Hradec Kralove Czech Republic

Institute of Innovation Science and Sustainability Federation University Australia Berwick VIC 3806 Australia

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