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

作     者：Zhu, Yu Zhang, Mingxu Huang, Qinchuan Wu, Xianbo Wan, Li Huang, Ju 

作者机构：Chengdu Sport Univ Sch Sports Med & Hlth Chengdu 610041 Peoples R China Hosp Chengdu Univ Tradit Chinese Med Chengdu 620010 Peoples R China 

出 版 物：《SCIENTIFIC REPORTS》 (Sci. Rep.)

年 卷 期：2025年第15卷第1期

页      面：1-24页

核心收录：

基　　金：School of Sports Medicine and Health 2024-2025 Research Excellence Program [ZYRC2406] Sports Medicine Key Laboratory of Sichuan Province [2023-A034] 

主　　题：Kernel extreme learning machine Secretary bird optimization algorithm Parameter optimization Diabetes classification prediction Quantum computing 

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 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:C$$\end{document} and bandwidth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:c$$\end{document} 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 approa