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<p>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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_87285_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:C\)</EquationSource> </InlineEquation> and bandwidth <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="41598_2025_87285_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="11" /> </InlineMediaObject> <EquationSource Format="TEX">\(\:c\)</EquationSource> </InlineEquation> 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.</p>

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Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年1月30日

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  • Yu Zhu,
  • Mingxu Zhang,
  • Qinchuan Huang,
  • Xianbo Wu,
  • Li Wan,
  • Ju Huang

摘要

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.