An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm
Abstract 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 compl...
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Nature Portfolio
2025-02-01
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| Online Access: | https://doi.org/10.1038/s41598-025-89348-8 |
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| author | Yuan Jin Yunliang Lai Azadeh Noori Hoshyar Nisreen Innab Meshal Shutaywi Wejdan Deebani A. Swathi |
| author_facet | Yuan Jin Yunliang Lai Azadeh Noori Hoshyar Nisreen Innab Meshal Shutaywi Wejdan Deebani A. Swathi |
| author_sort | Yuan Jin |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-e1c4e3de0efc4862a56319f70fb4e257 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-e1c4e3de0efc4862a56319f70fb4e2572025-08-20T02:15:10ZengNature PortfolioScientific Reports2045-23222025-02-0115111810.1038/s41598-025-89348-8An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithmYuan Jin0Yunliang Lai1Azadeh Noori Hoshyar2Nisreen Innab3Meshal Shutaywi4Wejdan Deebani5A. Swathi6Department of Cardiovascular Medicine, The Fifth People’s Hospital of GanzhouDepartment of Cardiovascular Medicine, The Fifth People’s Hospital of GanzhouInstitute of Innovation, Science and Sustainability, Federation University AustraliaDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa UniversityDepartment of Mathematics, College of Science and Arts, King Abdul Aziz UniversityDepartment of Mathematics, College of Science and Arts, King Abdul Aziz UniversityDepartment of Mathematics, Faculty of Science, University of Hradec KraloveAbstract 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.https://doi.org/10.1038/s41598-025-89348-8Heart disease predictionDeep learningSeagull optimizationRuzzo-TompaBoltzmann machineArtificial Intelligence |
| spellingShingle | Yuan Jin Yunliang Lai Azadeh Noori Hoshyar Nisreen Innab Meshal Shutaywi Wejdan Deebani A. Swathi An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm Scientific Reports Heart disease prediction Deep learning Seagull optimization Ruzzo-Tompa Boltzmann machine Artificial Intelligence |
| title | An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm |
| title_full | An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm |
| title_fullStr | An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm |
| title_full_unstemmed | An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm |
| title_short | An ideally designed deep trust network model for heart disease prediction based on seagull optimization and Ruzzo Tompa algorithm |
| title_sort | ideally designed deep trust network model for heart disease prediction based on seagull optimization and ruzzo tompa algorithm |
| topic | Heart disease prediction Deep learning Seagull optimization Ruzzo-Tompa Boltzmann machine Artificial Intelligence |
| url | https://doi.org/10.1038/s41598-025-89348-8 |
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