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

• Published in: Scientific reports Vol. 15; no. 1; pp. 3774 - 24
• Main Authors: Zhu, Yu, Zhang, Mingxu, Huang, Qinchuan, Wu, Xianbo, Wan, Li, Huang, Ju
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/1647; 692/163; 692/499; Algorithms; Birds; Chronic illnesses; Classification; Correlation coefficient; Diabetes; Diabetes classification prediction; Diabetes mellitus; Diabetes Mellitus - classification; Diabetes Mellitus - diagnosis; Humanities and Social Sciences; Humans; Kernel extreme learning machine; Learning algorithms; Machine Learning; multidisciplinary; Optimization algorithms; Parameter optimization; Prediction models; Public health; Quantum computing; Science; Science (multidisciplinary); Secretary bird optimization algorithm; Secretary bird optimization algorithm; Quantum computing; Parameter optimization; Diabetes classification prediction; Kernel extreme learning machine
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-87285-0

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 and bandwidth 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.