Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification
Abstract The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. G...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87285-0 |
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| _version_ | 1832571808050577408 |
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| author | Yu Zhu Mingxu Zhang Qinchuan Huang Xianbo Wu Li Wan Ju Huang |
| author_facet | Yu Zhu Mingxu Zhang Qinchuan Huang Xianbo Wu Li Wan Ju Huang |
| author_sort | Yu Zhu |
| collection | DOAJ |
| description | Abstract The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter $$\:C$$ and bandwidth $$\:c$$ of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes. |
| format | Article |
| id | doaj-art-a0409c1d6c14453f9150e7c4a2ff15f3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-a0409c1d6c14453f9150e7c4a2ff15f32025-02-02T12:17:38ZengNature PortfolioScientific Reports2045-23222025-01-0115112410.1038/s41598-025-87285-0Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classificationYu Zhu0Mingxu Zhang1Qinchuan Huang2Xianbo Wu3Li Wan4Ju Huang5School of Sports Medicine and Health, Chengdu Sport UniversityHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineSchool of Sports Medicine and Health, Chengdu Sport UniversitySchool of Sports Medicine and Health, Chengdu Sport UniversityHospital of Chengdu University of Traditional Chinese MedicineAbstract The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved Secretary Bird Optimization Algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the Secretary Bird Optimization Algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter $$\:C$$ and bandwidth $$\:c$$ of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approach offers an effective method for the early diagnosis and prediction of diabetes.https://doi.org/10.1038/s41598-025-87285-0Kernel extreme learning machineSecretary bird optimization algorithmParameter optimizationDiabetes classification predictionQuantum computing |
| spellingShingle | Yu Zhu Mingxu Zhang Qinchuan Huang Xianbo Wu Li Wan Ju Huang Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification Scientific Reports Kernel extreme learning machine Secretary bird optimization algorithm Parameter optimization Diabetes classification prediction Quantum computing |
| title | Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification |
| title_full | Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification |
| title_fullStr | Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification |
| title_full_unstemmed | Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification |
| title_short | Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification |
| title_sort | secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for kelm diabetes classification |
| topic | Kernel extreme learning machine Secretary bird optimization algorithm Parameter optimization Diabetes classification prediction Quantum computing |
| url | https://doi.org/10.1038/s41598-025-87285-0 |
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