Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease
Abstract Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity an...
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87598-0 |
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| author | Tiantian Bai Mengru Xu Taotao Zhang Xianjie Jia Fuzhi Wang Xiuling Jiang Xing Wei |
| author_facet | Tiantian Bai Mengru Xu Taotao Zhang Xianjie Jia Fuzhi Wang Xiuling Jiang Xing Wei |
| author_sort | Tiantian Bai |
| collection | DOAJ |
| description | Abstract Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity and mortality, but traditional prediction methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. The results of this study show that swarm intelligence algorithms can effectively screen key and informative feature sets, significantly improve model classification accuracy, and provide strong support for the early diagnosis of cardiovascular diseases. |
| format | Article |
| id | doaj-art-f620d22dfd22459886e85a763c2df7cd |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-f620d22dfd22459886e85a763c2df7cd2025-02-09T12:34:59ZengNature PortfolioScientific Reports2045-23222025-02-0115112110.1038/s41598-025-87598-0Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular diseaseTiantian Bai0Mengru Xu1Taotao Zhang2Xianjie Jia3Fuzhi Wang4Xiuling Jiang5Xing Wei6School of Nursing, Bengbu Medical UniversitySchool of Nursing, Bengbu Medical UniversitySchool of Nursing, Bengbu Medical UniversitySchool of Public Health, Bengbu Medical UniversitySchool of Health Management, Bengbu Medical UniversityDepartment of Computer Science, Bengbu Medical UniversityDepartment of Computer Science, Bengbu Medical UniversityAbstract Due to the aging of the global population and lifestyle changes, cardiovascular disease has become the leading cause of death worldwide, causing serious public health problems and economic pressures. Early and accurate prediction of cardiovascular disease is crucial to reducing morbidity and mortality, but traditional prediction methods often lack robustness. This study focuses on integrating swarm intelligence feature selection algorithms (including whale optimization algorithm, cuckoo search algorithm, flower pollination algorithm, Harris hawk optimization algorithm, particle swarm optimization algorithm, and genetic algorithm) with machine learning technology to improve the early diagnosis of cardiovascular disease. This study systematically evaluated the performance of each feature selection algorithm under different population sizes, specifically by comparing their average running time and objective function values to identify the optimal feature subset. Subsequently, the selected feature subsets were integrated into ten classification models, and a comprehensive weighted evaluation was performed based on the accuracy, precision, recall, F1 score, and AUC value of the model to determine the optimal model configuration. The results showed that random forest, extreme gradient boosting, adaptive boosting and k-nearest neighbor models performed best on the combined dataset (weighted score of 1), where the feature set consisted of 9 key features selected by the cuckoo search algorithm when the population size was 25; while on the Framingham dataset, the k-nearest neighbor model performed best (weighted score of 0.92), and its feature set was derived from 10 features selected by the whale optimization algorithm when the population size was 50. The results of this study show that swarm intelligence algorithms can effectively screen key and informative feature sets, significantly improve model classification accuracy, and provide strong support for the early diagnosis of cardiovascular diseases.https://doi.org/10.1038/s41598-025-87598-0Cardiovascular diseaseCuckoo search algorithmWhale optimization algorithmMachine learning |
| spellingShingle | Tiantian Bai Mengru Xu Taotao Zhang Xianjie Jia Fuzhi Wang Xiuling Jiang Xing Wei Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease Scientific Reports Cardiovascular disease Cuckoo search algorithm Whale optimization algorithm Machine learning |
| title | Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| title_full | Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| title_fullStr | Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| title_full_unstemmed | Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| title_short | Exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| title_sort | exploration and comparison of the effectiveness of swarm intelligence algorithm in early identification of cardiovascular disease |
| topic | Cardiovascular disease Cuckoo search algorithm Whale optimization algorithm Machine learning |
| url | https://doi.org/10.1038/s41598-025-87598-0 |
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